SELECTING AN OPTIMAL SET OF KEYWORDS FOR SEARCH ENGINE ADVERTISING

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1 International Journal of Industrial Engineering, 22(1), 62-79, 2015 SELECTING AN OPTIMAL SET OF KEYWORDS FOR SEARCH ENGINE ADVERTISING Minhoe Hur 1, Songwon Han 1, Hongtae Kim 1, and Sungzoon Cho 1,* 1 Deartment of Industrial Engineering Seoul National University, Seoul, Korea *Corresonding author s zoon@snu.ac.kr Online advertisers who want their website to be shown in the web search ages need to bid for relevant keywords. Selecting such keywords in advertising is challenging because they need to find relevant keywords of different click volumes and costs. Recent works focused on merely generating a list of words by using semantic or statistical methodologies. However, limited revious studies do not guarantee that those keywords will be used by customers and subsequently rovide large traffic volume with lower costs. In this study, we roose a novel aroach of generating relevant keywords by combining search log mining and roximity-based aroach. Subsequently the otimal set of keywords with a higher volume while minimizing costs was determined. Exeriment results show that our method generate an otimal set of keywords that are not only accurate, but also attract more click volume with less cost. Keywords: search engine advertising; ad keywords; query logs; knasack roblem; genetic algorithm 1. INTRODUCTION (Received on November 30, 2013; Acceted on December 29, 2014) Search engine advertising is a widely used business model in the online search engine system (Chen et al., 2008, Shih et al., 2013). In this model, advertisers who want their ads to be dislayed in the search results age bid on keywords that are related to the context of ads (Chen Y. et al., 2008). The ads can be dislayed when the corresonding keywords are searched and their bid rices are higher than the minimum threshold (Chen et al., 2008). It is demonstrated that this business model offers a much better return on investment for advertisers, because those ads are resented to the target users who consciously made search queries using relevant keywords (Szymanski et al., 2006). Figure 1 shows the examle of search engine advertising where advertisements are dislayed on the result age followed by a query. To bid on keywords, advertisers need to choose which keywords would be associated by considering their ads that will be dislayed (Ravi et al., 2010). In general, there are three criteria widely known that aly to good keywords. First, advertisers need to select relevant keywords that relate to their advertisement closely so that many otential customers would query those keywords to find their roduct or services (Kim et al., 2012). It is the most imortant ste for reducing the ga between keywords selected by advertisers and their otential customers (Oritz-Cordova and Jansen, 2012). Secondly, choosing keywords that attract larger volume of clicks toward their advertisements among relevant keywords will be more desirable (Ravi et al., 2010). As keywords have their own click volume in the search engine, selecting them to increase the number of clicks on their ads as ossible is one of the critical elements in search engine marketing. Finally, when comaring a grou of keywords that are relevant and oular, identifying and selecting keywords that are cheaer than others will be also desirable to imlement, more efficient and an effective marketing camaign with limited budgets. However, selecting keywords manually by considering such criteria is a challenging and time-consuming task for advertisers (Abhisher and Hosanagar, 2007). For one, it is difficult to determine which keywords are relevant to the target ads. Though advertisers generally have a good understanding over their ads, their desire is to select keywords that would not only reresent their ads well but also be used by otential customers who would ultimately be interested in the roducts or services they offer. Moreover keywords have volatile click volumes and cost-er-click influenced by user search behavior in search engine for a long time. Therefore it is not easy to grow influx of customers into their websites while reducing costs at once. To overcome the raised roblems, many studies have been roosed and they can be divided into two categories: (1) Generating related keywords by develoing certain automatic methods for generating related keywords so that advertisers would find suitable keywords more easily and (2) Selecting an otimal set of keywords to maximize the objective values such as click volume or ad effects with budget constraints. Though such efforts work well in their own exeriments, they have several limitations to be widely alied in real roblems. First, some studies have no guarantee that the keywords would actually be queried by users. Generated keywords should be familiar to not only advertisers but also otential customers so ISSN X INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING

2 that the search results and the user s intention of searching behavior will match each other (Oritz-Cordova and Jansen, 2012). Second, most studies utilized artificial data for evaluating the roosed methods. Though they have shown the better erformance than revious studies, it was doubt that roosed methods would also work well in real alications. Third, limited revious studies incororate how much the keyword would increase click volume or decrease cost-er-click. Instead, most works just accet the limitations of their study or assume that all keywords will have identical volume and cost. Fourth, there were limited trial to combine the method from both categories. Most studies focused on either generating keywords or finding an otimal bidding strategy in their own category. Though better erformance by the roosed model would is an imortant issue, the more imortant for marketer is ractical aroach from generating to selecting keywords in their advertising. Therefore, it can be concluded that revious studies have limited imlication in alying to real alications. Figure 1. An examle of search engine marketing on Google result age queried by Digital marketing In this study, we roose a novel method for selecting a set of keywords that can overcome such limitations from revious works. To generate relevant keywords, we gather candidate keywords from search engine logs. Since those keywords are linked in query logs, we establish them in a directed network with node as a keyword and directed edge as relevance. And we generate a similarity matrix between keywords and target context by combining network-based similarity (search log mining method) and word roximity-based method. Then to find a keyword set that had lower click costs but higher click volume within budget constraint from candidates, we transform into 0/1 Knasack roblem and alied metaheuristic aroach for achieving aroximated otimal solutions. To evaluate our aroach, we erform tests on three different advertisement camaigns, and then comare the results to other methods by qualitative and quantitative assessments. We exect our study would give business imlications to generate search engine keywords for advertisers. First, it resents a novel method to generate keywords not only widely used by users but also related to their ads. So we exect any advertisers who want to generate keywords for search engine advertising can easily get the effective candidates by using roosed method from search engines such as Google, Yahoo or Naver. Second, it also resents how to select among candidate keywords based on budgets to maximize influx of users but minimize total costs for ads. Advertisers need to adjust raidly their advertising strategy by considering the changing market environment. By alying our roosed otimization method eriodically to get the best solution with volatile values of keyword, we exect advertisers would otimize their advertisement strategy within the marketing budgets. Third, we rove our roosed aroach can be a good solution by comaring results to other methods after we aly them to the real-world alication. From the generation of keywords in three different advertisement camaigns, finding a set by otimization techniques to the comarison with other methods, we strictly validate how the roosed aroach would be alicable with the analysis of results and limitations. This aer is organized as follow: in section 2, we discuss related work that shares similar research objectives to our study; section 3 exlains novel techniques from exloration of related query terms to selecting a set of keywords. We describe exeriments and evaluations of our method in section 4. We summarize what we have done and discuss further ossible studies in section RELATED WORK This section exlores related studies that have been develoed for search engine advertising. Their efforts can be divided into two categories: (1) Keyword generation and (2) Finding otimal set of keywords. And each category is discussed with introducing revious studies. 63

3 First category is to generate relevant keywords. The query log mining method has been widely used in services rovided by search engines. This method focuses on co-occurrence relationshi between keywords and suggests similar words to the initial keyword (Abhishek and Hosanagar, 2007). Many oular search engines such as Google Adword Tool or Naver Keyword Generation System have adoted this method using the vast query history of numerous users stored in their database (Shih et al., 2013). Comared to other methods, this aroach can resent the words which had been widely queried by actual users. The roximity-based method sends initial keyword to the search engine and obtains relevant words from highly ranked webages (Joshi and Motwani, 2006). The roximity of the words is based on whether such words had included the initial keyword or not. For examle, this method will find relevant keywords such as Ale imac or Ale iphone when the keyword Ale are queried as the initial term. This aroach can generate relevant keywords retty well. However, it is not ossible to identify relevant keywords that do not contain the initial term (Abhishek and Hosanagar, 2007). Also the number of generated keywords is less than other methods because relevance by containing the initial term and showing similar meanings together are not casual. Semantic relationshi is another oular aroach. TermsNet is a grah-based technique to identify terms that are highly relevant but not obvious (Joshi and Motwani, 2006). In this study, each term is resented by using a characteristic document. Then, those gathered terms are sent to directed grah, where each node reresents a term and a directed edge reresents a relevance relationshi. In the grah of TermsNet, keywords are generated to a given initial term by following outgoing as well as incoming links. Also, there was a trial to utilize knowledge reositories to gather relevant keywords (Jadidinejad and Mahmoudi, 2014). In this work, they argued that generated keywords need to be relevant to the base query but they should not be obvious and exensive. And they tried to gather keywords from Wikiedia which contains information about diverse and recent toics. With word sense disambiguation and language model to secify the semantic meaning of gathered keywords, they argued that the roosed method would generate exert keywords with efficient comutation comared to Wordy. To summarize the aroach of semantic relationshi, they would find the non-obvious keywords. However, it is not clear that those words will actually be widely used (Wu et al., 2009). Wordy is develoed to suggest keywords that are relevant while having lower frequency so that bidding cost will be lowered (Abhishek and Hosanagar, 2007). After alying a similar aroach to TermsNet for finding semantic similarities between keywords in the corus, Wordy suggests keywords that are not only semantically relevant but also less frequently used by assuming that low frequent words will be cheaer. However, considering cost alone can lead to a decrease the click volume since lower cost keywords might be too far away from the initial contexts (Sarmento et al., 2009). To summarize the efforts in this category, there are some limitations that revent those studies to be effectively utilized by advertisers. First, excet for query log mining method, there are no guarantees that the keywords generated would actually be queried by users. In other words, we need to consider the keywords that have been widely used so that the search results and the user s intention of searching behavior would match each other (Oritz-Cordova and Jansen, 2012). Second, costs and click volume are merely considered for final keywords set (Hui et al., 2013). Each keyword has unique cost-er-click and click volume in the search engine. Therefore, the advertisers must carefully bid on keywords by shrewdly rojecting how much the total costs would be based on their budgets and how many clicks will be rovided for their ads. Instead, many works just ignore them or assume that all costs or click volume for each keyword are the same. Second category is to find an otimal set of keywords to maximize click volume with budget constraints. Tran-Thanh (2014) roosed a stochastic market rice model for bid otimization in keyword selection to maximize the number of clicks with limited budget. By considering the lack of information about bids in search engine advertising, they roosed model to estimate the distribution of bid rice with historical data from Microsoft adcenter. The bidding strategy with their roosed method was exected to achieve good erformance in the simulation. Beyond the stochastic aroach, there were efforts to aly deterministic otimization techniques to get desired exosures while minimizing total costs (Selçuk and Özlük, 2013). With diverse variables such as beta distribution to reresent the ad osition, click-through-rates, exosures and imressions, they established several non-linear otimization equations to maximize the exosures by satisfying constraints. However, they just imlemented their models to artificial dataset which made the model difficult to guarantee the similar erformance in the real alication. Another aroach in the category of finding an otimal set was utilizing econometric models. This aroach was based on the theory for the human behavior related to economic decisions such as Nash equilibrium or Game theory. And it resented strong mathematical foundations with the assumtions. Koh (2013) roosed the otimal solution of bidders with equilibrium model in keyword auctions when the budget constraints were given. Similar technique was found from the work of Chaitanya (2010). With budget constraints, they tried to solve the two roblem: budget otimization and bid otimization. To get the otimal set of keywords with budget limit, they alied linear rogramming. And the otimal set was sent to find the maximum return on ads for advertisers. However, their efforts were based on theoretical roofs or imlementation with simulated data. The last study in the category was not only generating keywords but also finding an otimal set of keywords by Zhang (2014). And their efforts were the most influential to this study. Authors argued that most advertisers tried to bid on oular 64

4 keywords. And this strategy made them fail to get satisfactory results since those oular keywords were exensive and most advertisers had no chance to lace their ads on search results. They also insisted that those behaviors would low the rofit of search engines because slots other than oular keywords are emty. To find the relevant but chea keywords, they analyzed the big data of query logs and click atterns from search engine (Bing.com). After finding that the number of queries for each keyword showed long-tail henomenon, they recommended a set of relevant but less-cometitive keywords. The final otimal set was found by integer rogramming with constraints. To summarize the second category, those conclusions would be followed. First, excet some studies, most of them did not consider generating relevant keywords. They just assumed that keywords were given or ignored finding relevant keywords. Instead, they focused on how to make managerial decisions in bidding keywords to maximize the objectives in a given constraint. Second, most studies utilized artificial data for evaluating the roosed methods. Though they have shown the better erformance than revious studies, it was doubt that roosed methods would also work well in real alications. 3. METHOD The rocess of keyword generation can be organized into three stes. First, we exlore a large number of related keywords from an initial set of keywords and resented them in a directed grah. Second, keywords that are more relevant to the initial keyword are gathered from the grah. Third, a set of keywords is selected by considering exected click volume and click costs. Figure 2 shows our framework consisting of these three stes in detail Exloration of Related Query Terms To generate keywords, we need to generate candidate terms for an initial keyword. In our method, we focus on Related Query Terms from query logs in the search engine. Related Query Terms or co-occurring query airs are words that have been commonly searched right after a former word is searched (Boldi et al., 2009, Huang et al., 2003). For examle, if Beach Towel and Towel for resents have been queried for a large number of times after Towel was queried, then Beach Towel and Towel for resents are regarded as Related Query Terms of Towel. Former searched word and its corresonding Related Query Terms have a close relationshi with each other, as users who tried to get the contents usually search again by changing terms while maintaining the same context (Boldi et al., 2009, Liao et al., 2013). Comaring to other methods of gathering candidate terms, this aroach have several advantages. First, since Related Query Terms are based on query logs that are actually queried by users, we can get terms that have a higher chance to be queried and consequently increase the click volume to the advertisement. Second, we can easily get them not from indirect estimation such as statistical similarity, but from direct estimation from the semantic flow formed by cumulated query history of many users. To gather Related Query Terms, we followed query-flow grahs (Boldi et al., 2009) in the query logs from search engine. First we query an initial keyword in the search engine that is well-reresented in the context of advertisement. Then we crawl corresonding Related Query Terms from query logs in the search engine database. Next, each term from Related Query Terms we obtain is queried again and in the same way, corresonding related keywords are also crawled. By reeating this rocess a few times, we could obtain a large number of keywords within a few iterations. And we rune the grah when the level of deth from an initial word grow to be higher than a certain threshold. Those consecutive searches can be resented by a directed grah, from a former term to its Related Query Terms. Ste 1 in Figure 2 shows the directed grah with the term as a node and the relation as a directed edge Relevant Keyword Generation Though we can identify relevant keywords from query logs, not all terms are related to the initial keyword. As the keywords drift far away from the initial keyword, the relevance seems to get lower. It means that there are certain oints where context of an initial keyword is changed as it moved away. An examle of related keyword shows the directed grah like Songwol towel 1 Towel for resents Photo Studio Photo studio for baby. Though Photo Studio is one of Related Query Terms of Towel for resents, the term is deemed irrelevant to the towel roducts. This fact makes us recognize the need to extract keywords related to the initial word from the grah. 1 Name of towel brand in Korea 65

5 Figure 2. Framework of finding a set of keywords To extract only the significantly relevant keywords, we exlore how each keyword is linked to others in a keyword network. From this, we note that two words sharing same former query terms (in-links) or same related query terms (outlinks) show greater similarity than other words. Also two keywords are more similar than others if they have a common word. Therefore we can set the similarity criteria as two keywords are similar if they are related to similar keywords (networkbased similarity (NSim)) or they share a same word (word roximity-based similarity (WSim)). And this theme of similarity can be directly imlemented to the combined similarity measure (CSim) in a network as Equation (1) by linearly combining two similarity concets for keyword a and b. ( a b) = α R ( a, b) (1 α) R ( a b) CSim, NSim WSim R, (1) Equation (1) can be regarded as a hybrid aroach of combining query log mining aroach and roximity-based aroach of revious works. First comonent can be generated from a structure of query logs by considering how keywords are linked together and second comonent has same definition of roximity-based aroach. So we exect that this hybrid aroach would extract relevant keywords in structural and literal sense from a directed network. In the network-based similarity (NSim), many similarity measures can be considered. Here, we aly P-Rank algorithm (Zhao et al., 2009) originally develoed for resenting structural similarity measure over the information network. For keyword a and b in directed keyword grah G, let R l ( a, b) denotes the similarity score on iteration l for a b and RCSim, l ( a, b) = 1 for a = b. And let I (v) and O (v) denote the set of in-link neighbors and out-link neighbors of vertex v, resectively. If in-link and out-link is balanced through the arameter λ [0,1] R l 1( a, b) is udated recursively from R l a, b with a daming factor C [0,1] as follows: revious value of ( ) R CSim, l 1 I( a) I( b) (1 α) R I ( a) I ( b) O( a) O( b) C C ( a, b) = α λ RCSim, l ( Ii ( a), I j ( b) ) ( 1 λ) RCSim, l ( WSim ( a, b) i= 1 j= 1 O( a) O( b) i= 1 j= 1 Oi ( a), O (2) In the roximity-based similarity (WSim), we directly imlement the definition from revious work. For an initial keyword or concet k with two keywords a and b, WSim between them has v > 0 if both keywords have k in their words and 0 otherwise. Similarity score v can be determined by emirical results or rior knowledge. Here, we set v = 0. 1 so that WSim would not be dominant too much in the combined similarity. Ste 2 in Figure 2 shows the relevant keywords and their corresonding similarity results calculated by roosed methods. Figure 3 shows the algorithm of our roosed aroach by considering two similarity measures. Here, line 1 to 6 shows the initialization ste to WSim matrix and calculating WSim. Between line 7 and 19 shows how the NSim between keywords are calculated. And WSim and NSim are combined in line 20. The last three lines show that calculated similarity is udated from every iteration. Note that the CSim in line 23 is used in calculating NSim (line 14 and 18) of the next iteration. 66

6 It shows that the combined similarity is not merely weighted sum of two similarity measures, but reinforcing similarity value to the adjacent nodes. Based on the algorithm, we can generate relevant keywords by filtering out keywords that scores less than a certain level and the result is attached in the Evaluation section with more details Finding a set of keywords Figure 3. Algorithm of calculating similarity between keywords Using all the relevant keywords at the same time for advertising will incur too much cost, though it would maximize the click volume. As such, the final keyword set should be carefully selected from those keywords according to available budget constraints given to ay for advertising. To get the final keyword set, we need to consider two imortant criteria: the average click volume and cost-er-click of each keyword. When the keyword is queried, average click volume is a measure of average number of clicks occurred and cost-er-click is the average cost occurred by one click of a keyword. We define that selecting the most efficient bidding keyword set roblem is equivalent to select the keyword set which maximizes the sum of exected click volume and minimizes the costs er click within a certain budget. Hence, we define the score as a measure for efficiency of each keyword in Equation (3), reflecting the average cost and click volume. As click volume and cost will change in time line, we set to average them from revious month ( t 1 ) and regard them as unchanged in the next month ( t ) by considering the common fact that alying the keyword set into search engine marketing has been udated by monthly. It is remarkable that average click volume and cost-er-click values for each keyword were gathered from web search engine where the advertisement would be laced. Though Naver which is the largest search engine in Korea was selected as a data source in this study, any web search engine that rovides click volume and cost-er-click of keywords can also be selected. 67

7 Average Click Volume score = Average Cost er Click.(3) Using the score for each keyword, we need to find the best set of keywords among the many kinds of ossible combinations. As the total cost of selected keywords should not be exceeded the budget of the bidding, we can reresent this objective and restriction in a mathematical form, esecially the 0-1 Knasack roblem in Equation (4) where s is the score and c k is bidding cost of keyword i k i, and B is the budget. ki Maxmize n s ki i= 1 x k i subject to n i= 1 c k i x k i B x k i 1 = 0 if keyword k otherwise was selected i (4) However, finding an otimal solution of the roblem will not be solved in olynomial time because this roblem is the well-known examle of NP-Hard (Ko et al., 2013). Many techniques to find the aroximated otimal solution have been develoed. Here we aly a meta-heuristic algorithm for 0/1 Knasack roblem with Genetic Algorithm to find the aroximated otimal combination of keywords. Genetic algorithm (GA) is stochastic search method based on the mechanism of evolutionary algorithm such as natural selection and has been widely alied to solve oeration research roblems during the last decade (Moon et al., 2008; Lai et al., 2012; Kovac, 2014). GA begins with initial oulation and it is comosed of randomly generated chromosomes. Each chromosome stands for ossible solution to the roblem and is evaluated by fitness function. As genes that are more adative to the environment have more chance to be survived in nature, so some chromosomes are selected with the chance roortional to the fitness value in GA algorithm. Offsring are generated by several oerations such as crossover or mutation. And those new chromosomes are added to oulation and stes above are erformed iteratively until some sto conditions are satisfied. Figure 5 (a) shows the flow diagram of GA algorithm secified to solve our roblem and Figure 5 (b) shows how the fitness value from each chromosome was calculated. And we exlain how our roblem was converted to GA roblems and configurations. Initialization: We set the size of initial oulation as 150 and each chromosome is randomly generated based on the constraints. Any chromosome that does not obey the constraints is deleted and generated until all of them were satisfied. Reresentations: Chromosome in our study is based on one dimensional array of binary values. Here, each value reresents whether each keyword is inserted in the set as 1 or not as 0. Figure 4 shows the examle of a chromosome reresentation. Definition: For each keyword k Uk, Uk = n where U 0 0 k is the set of all candidate terms from 0 context or initial keyword k 0, set of keywords S i is exressed as a chromosome which is consisted of items that have 1 if k Si or 0 if k Si. chromosome i x k 2 x k 3 x kn x k 1 Figure 4. Reresentation of chromosome in genetic algorithm for 0/1 Knasack roblem Oerations: Three oerations such as selection, crossover and mutation are used. From oulation in each generation, individuals are selected by stochastic uniform selection method. We roduce offsring by a uniform crossover with robability 0.5. And mutation is alied randomly to each item so that it would not be over the constraints. Stoing rules: The iterations are stoed if the average change in the fitness function value over 100 generations is less 6 than 10. Fitness functions: The fitness value stands for how much the generated solution from the objective function would be good under the constraints. In our study, it is established so that the aggregated scores from each ossible keyword set would be maximized. So we exect that the solution set will contain keywords with higher click volumes while lower click costs. 68

8 4. EVALUATION To evaluate our roosed method, three different initial keywords that come from different contexts are selected, which are: Songwol Towel, Imlant, and Thermos. They have been bid by many advertisers in Korean web ortals. Related Query Terms of each initial keyword are crawled from search engine API 2. We rune the networks where terms are linked to the initial keyword away of 4 levels. (a) (b) Figure 5. (a) Flow chart of 0/1Knasack genetic algorithm and (b) Flow chart of fitness function We comare all of the results obtained from our roosed method (Hybrid) and two revious methods such as query log mining with P-Rank (QLM-P) and roximity-based method (PB) as a baseline. Those three models are easily converted to each other by changing arameter α in Equation (1). Also we comare it to the results of Naver Keyword Generation System 3 (NKGS), which has been rovided by Naver, the to search ortal service in Korea. One of the reasons to select NKGS in the evaluation is that this system has been used by many advertisers in the real business world. Also, it has been ranked as the best search engine marketing in the nation, ranking higher than Google or Yahoo in Korea. Therefore, by comaring our method to NKGS, we exect to see how cometitive our method would be in the search engine market. We summarize several methods to be evaluated in Table 1. All the arameter values have been determined based on the revious works (Zhao et al., 2009) and emirical studies so that each method shows the best results with the balanced two similarity measures. Table 1. Models and their descritions to be evaluated Models Descrition Parameters in Equation (1) and (2) α λ C NKGS Generating keywords from Naver Keyword Generation System QLM-P Query log mining method with P-Rank Hybrid Proosed method PB Word roximity-based method Naver Oen API (htt://dev.naver.com/oenai/ais/search/recmd) 3 Naver Keyword Generation System (htt://searchad.naver.com) 69

9 Based on each method, different keywords are generated to each initial keyword. For NKGS, we collected keywords generated by the system after we queried initial keywords. For others including Hybrid method, we use Related Query Terms as a common dataset and relevant keywords are gathered by each method. Table 2 shows the descrition of dataset. QLM-P and Hybrid method utilize all the Related Query Terms while PB only uses limited keywords that have the initial keyword in it. Table 2. Data descrition: Number of keywords for each initial keyword Initial Keyword Songwol Towel Imlant Thermos Method NKGS QLM-P Hybrid PB NKGS QLM-P Hybrid PB NKGS QLM-P Hybrid PB Number of selected keywords Keyword Generation We can generate many relevant keywords by different methods for each exerimental keyword. For the sake of brevity, we select five suggestions by our roosed method and listed in Table 3. To assess the quality of generated keywords, we randomly select about 200 keywords for each camaign. As the relevance cannot be determined by an automatic method, we aly qualitative assessment judged by human. 5 human evaluators are asked to check whether each keyword is relevant to the target context of ads. All of evaluators are eligible to erform such tasks and are familiar with the requirements of the evaluation rocess. Based on their evaluations (relevant if 2 of 5 evaluators agree), recision and recall criteria are used to validate the quality of suggested keywords. In Table 4, we can see that PB generates the most accurate keywords than others. However, it shows low recall in all the dataset since the number of generated keywords is limited. NKGS rovides irrelevant keywords in every dataset. On average, our roosed method shows the best F-measure value than others. Table 3. Generated keywords by Hybrid method for each initial keyword (Translated to English) Songwol Towel Imlant Thermos Keywords Similarity Keywords Similarity Keywords Similarity Songwol Towel Imlant Thermos Songwol Towel Store Imlant side effects Thermos for baby Songwol Towel for resents Imlant ain Zojirushi thermos Towel with Cake-style Imlant rice Thermos recommendation Songwol Towel for kitchen Imlant Insurance Elehant thermos Table 4. Precision, Recall, and F-measure for generated keywords Methods NKGS QLM-P Hybrid PB Criteria Initial Keyword for advertisements Songwol Towel Imlant Thermos Average Precision Recall F-measure Precision Recall F-measure Precision Recall F-measure Precision Recall F-measure

10 4.2. Exected Total Clicks and Cost-er-click by genetic algorithm We comare the exected total clicks and cost-er-click for all keyword set. For each keyword set, 0/1 Knasack algorithm with GA is alied to find a best set of keywords based on several ossible budgets. The algorithm is iterated 10 times for each keyword and budget. All the results are averaged by iteration times. Figure 6. Exected total clicks for each keyword set based on different budgets: (a) Songwol towel, (b) Imlant, and (c) Thermos Figure 7. Exected cost-er-click for each keyword set based on different budgets: (a) Songwol towel, (b) Imlant, and (c) Thermos Figure 6 shows the aggregated clicks from different methods for each budget. For each keyword, Hybrid method attracts more exected click volumes than other methods from (a), (b) and (c). It shows that the keyword set generated by our roosed method is exected to extract more click volumes than other methods when they are used in search engine marketing. Also, (a), (b) and (c) in Figure 7 indicate that exected cost-er-click by selected keywords are also lower in our method than others. It suggests that the keywords selected by our method are of lower cost on average when they are queried and the link to ads is clicked. Those findings show the imlications that generating keywords from Related Query Terms is one of good aroach that attracts many clicks with low costs. Also, relevant keywords to the initial keyword by combining structural similarity and word similarity would erform better than utilizing each similarity searately. PB, which uses the word similarity only, shows no gains of clicks when the budget increases because the number of keywords is not sufficient. Though QLP-P shows the similar atterns to our method, our method gather more with higher clicks with lower cost from PB and it makes the difference between them. As such, we can conclude that our method can generate relevant keywords with higher click volume while maintaining lower cost er click. To check whether the emirical otimal solutions will show robustness, we aly sensitivity analysis for each arameter of our best exerimental result and attach the analysis results in Aendix Comaring Results from GA and Other Aroximation Algorithm for Solving 0/1 Knasack Problem To find the set of keywords by considering click volume and budgets all together, we converted the roblem into 0/1Knasack roblem. And the roblem is known as NP-hard roblem which is no olynomial deterministic algorithm to 71

11 solve NP-hard roblem but all ossible combinations should be traversed to get the otimal solution. Though we can easily get the otimal solution if the size of inuts is relatively small, it is almost imossible to get it if the size is big and the ossible combination increases exonentially. Figure 8. ε-knasack algorithm for aroximating otimal solution of 0/1 knasack roblem Fortunately, many researchers have develoed non-deterministic algorithm that oerates in olynomial time to find the nearly otimal solution without considering all the ossible inuts. One ossible way is to aly heuristic algorithm such as GA: it heuristically finds feasible solutions from all combinations in a more efficient way. We aly this method in our study and showed satisfactory results in the budget limits. Another way is to aly aroximation algorithm. Aroximation scheme guarantees the difference between objective value of an otimal solution and a feasible solution generated by the aroximation algorithm is less than certain constant. Among the aroximation algorithms, we aly ε-knasack algorithm roosed by Shani (1975). This aroach shows the ercentage error between otimal and feasible solution is less than ε = 1 (1 k) where k is ositive integer. Also it shows a comuting time exressed by olynomial in the roblem size ( n ) ( 1/ ε ) O where n is an inut size. Figure 8 shows the ε-knasack algorithm. In this chater, we do another exeriment to rove whether there are differences between the results from each algorithm. To comare the erformance between GA and ε-knasack, first we find the keyword set by each method. Second, as we do exeriment in comaring keyword generation methods in chater 4.2, we do again for each algorithm. To show the results in one grah, we subtract the results when GA is alied to when ε-knasack is alied. Figure 9 shows bar chart for the budget range on x axis and the differences on y axis. We set k = 2 in ε-knasack by considering time and memory costs of exerimental environment. We can see that keyword set found by GA in general earned more exected total clicks than ε-knasack regardless of keyword generation method. Though ε-knasack shows better results when the budgets is less than 5,000,000 in Songwol Towel, the ratio is far below than 0.5. Figure 10 shows bar chart for the same range of budgets but the differences of cost-er-click on y axis. In most cases, GA can find keywords in the set that have a cheaer cost-er-click than ε-knasack. In (a) and (b) of Figure 10, we can see some abnormal bars have much greater than others. After analyzing the reason, we find that it haens to fail to add more keywords even the amount of costs is much less than budgets when the ε-knasack was alied. So the keyword set remains unchanged even the budget is increased and it also causes the cost-er-click remained same, which is usually small value. Though we can see that GA is better choice to maximize click volume toward advertisement while minimizing cost-erclick, there is the some budget range (e.g. from 1,000,000 to 5,000,000) where ε-knasack algorithm shows better in both of clicks and cost-er-click. To know why, we analyze how the keyword scores, which are to be maximized in objective function, are distributed. Table 5 shows the descritive statistics of score distribution from keywords generated by different methods. Here we focus on standard deviation and skewness which are measure of how the scores are nonhomogeneous and unbalanced in the range of score values. Emirically, we can conclude that the deviation and skewness of scores are significantly related to the erformance of otimization algorithms. When both statistics are higher, GA is more likely to be locked in the local otima and shows inferior results. For examle, in the case of Songwol Towel, the erformance of GA is not better in the Hybrid and QLM-P methods which have higher deviation and skewness than other methods. Also in the case of Imlant and Thermos, the 72

12 Hybrid and QLM-P methods have more chance to get minus value in Figure 9 and 10 than others as they have higher deviation and skewness too. So this result suggests that the selection of otimization algorithm should be carefully done by considering the distribution of values in the objective function. In our emirical results, it is thought that ε-knasack algorithm would be a better choice when the data is highly unbalanced and skewed but the budget is small. Figure 9. Difference of exected total clicks from alying GA and ε-knasack Algorithm Figure 10. Difference of exected average cost-er-click from alying GA and ε-knasack Algorithm Table 5. Descritive statistics of scores from keyword set generated by different methods Keyword Method Mean Standard Deviation Skewness Hybrid Songwol Towel PB NKGS QLM-P Hybrid Imlant PB NKGS QLM-P Hybrid Thermos PB NKGS QLM-P To conclude the exeriment results, we can see that GA have more chance to find the nearer otimal solution than aroximation algorithm to solve the 0/1 knasack roblem when we comare the exected click volume and cost-er-click. Also we can see another ro of GA that it erformed much faster than ε-knasack algorithm when the arameter k is less 73

13 than three. That reason is ε-knasack algorithm requires comlexity to generate all ossible k combinations to be acked ( 1) rior to solve the traditional 0/1knasack roblem: the time comlexity of ε-knasack is O ( n k ). Figure 11. Comaring exected total clicks after alying GA and ε-knasack algorithm However, we cannot conclude that GA is always better than others when the distribution of scores have relatively higher variance and highly skewed. The most critical roblem is that it generates different results by every exeriment, since it is based on the randomness. Figure 11 shows box lot of total clicks when we iterate 10 times of alying GA. Comare to the result of ε-knasack algorithm that show deterministic value regardless of trials, we can see deviations. To minimize this roblem, average results from several trials as we do in this study can be considered. Then we exect the result would be more robust and confident. 5. CONCLUSION AND FUTURE WORK In this study, we have roosed a novel aroach for selecting a set of keywords in search engine advertising. For a given target keyword, Related Query Terms are crawled from search logs, which are considered as candidates for the final keyword set. After resenting them into relational structures of a directed grah, hybrid aroach is alied to find the relevant keywords. The final ste is determining the best set by considering average click volume and cost-er-click for each keyword by genetic algorithm to solve the 0/1 Knasack roblem. Using three different initial keywords, we erform exeriments comaring the results of our method and other revious methods over the accuracy, average click volume and cost-er-click. From the exeriments, it is shown that our method successfully generate keywords that are not only relevant but also attract more click volume with lower costs. To validate how the erformance can be different according to otimization techniques, we comare two aroaches: GA of heuristic algorithm and ε-knasack algorithm of olynomial time aroximation scheme. In a small k which is 2 in our exeriment, GA has more chance to find a solution which is nearer to otimal regardless of keyword generation method and advertisement camaigns. Also GA erformed much faster than ε-knasack algorithm when k is greater than 3. However, it should be noted that GA generates different results by trials and might fail to find a reasonable solution according to data characteristics such as skewness. Overall, if the roer methods to minimize the cons of GA are rovided, we exect that GA can be regarded as a better solution to find the keyword set. We exect our study has several industrial alications. First, we roose a novel hybrid method to generate relevant keywords that not only related to ads but also widely used by customers. We have gather candidate terms from query logs that have been widely used reviously by users and extract relevant terms by combining network based and roximity-based similarity. The result roves that our method receives the highest F-measure score on average than two revious methods and one advertisement services available in the real world. So we exect advertisers can get more effective keywords for their ads if our roosed method could be considered. Second, our study rooses the method to select the keyword set by considering click volume and costs that each keyword had. It is ractical for advertisers to lan their online advertising effectively and efficiently with limited budgets. Third, we validate our roosed aroach in the real world alication by comaring results to other methods. So we show ractically how to aly it in the ad camaigns and what is better than other revious methods. Though we try to roose the novel method that can overcome the limitations of revious studies as much as ossible, there are still remaining future works to be covered. First, a faster similarity algorithm needs to be alied. While the algorithm is successfully used in our aroach, one of its main roblem is its comlexity: O ( n 4 ). One ossible way to do it is to use the runing technique roosed by Jeh and Widom (2002) alied to SimRank, which is similar to our algorithm. Second, keywords that are semantically similar can be also used as candidate keywords. Though our aroach uses relevant 74

14 keywords in which relations among them have been formed by query logs accumulated for a long time, one of its limitations is that it often fails to identify keywords that are not only commonly used in search engine but also semantically same to the target keyword. If we utilize diverse similarity measures, we are certain that a greater number of keywords that are relevant and not obvious can be generated. Third, diverse factors need to be considered to solve the ractical roblem in search engine marketing. To focus on the generating keywords and finding otimal keywords, bid rice or ad osition was merely considered in this study. However, bid rice to the ads be shown in the search results and ad osition among slots are imortant factors for advertisers to finely maniulate the effects of advertisement. Another factor such as urchase conversion of the keyword can be also considered (Rutz et al., 2012). Though keywords that can attract more click volume would be romising, they would not guarantee that customers visited the ads with them actually buy the roducts. Therefore, if the urchase log data can be gathered from ad ages or websites, advertisers would make managerial decision by considering the sales rofit with keyword costs. Finally though we consider two otimization techniques, other ossible algorithms can be also comared and analyzed. The use of genetic algorithm is romising because it is good to find a set of otimal set of keywords. However, if the number of candidate keywords is small, we can consider to try to get the exactly otimal solution with the high comuting ower and comare what the difference is. Also if the guidelines of choosing algorithm by data or other features could be roosed, we hoe that the researchers and ractitioners who have similar roblem to us would have valuable knowledge in the future works. ACKNOWLEDGEMENT This work was suorted by the Brain Korea 21 PLUS Project in , the National Research Foundation(NRF) grant funded by the Korea government(msip) (No ), and the Engineering Research Institute of SNU. REFERENCES Abhishek, V., & Hosanagar, K. (2007). Keyword generation for search engine advertising using semantic similarity between terms. In Proceedings of the 9th International Conference on Electronic Commerce, New York, New York, USA: ACM Press. doi: / Boldi, P., Bonchi, F., Castillo, C., Donato, D., & Vigna, S. (2009). Query suggestions using query-flow grahs. In Proceedings of the Worksho on Web Search Click Data, ACM. doi: / Chaitanya, N., & Narahari, Y. (2012). Otimal equilibrium bidding strategies for budget constrained bidders in sonsored search auctions. Oerational Research, 12(3): doi: /s Chen, Y., Xue, G.-R., & Yu, Y. (2008). Advertising keyword suggestion based on concet hierarchy. In Proceedings of the International Conference on Web Search and Web Data Mining, ACM. Huang, C.-K., Chien, L.-F., & Oyang, Y.-J. (2003). Relevant term suggestion in interactive web search based on contextual information in query session logs. Journal of the American Society for Information Science and Technology, 54(7): doi: /asi Hui, K., Gao, B., He, B., & Luo, T. (2013). Sonsored Search Ad Selection by Keyword Structure Analysis. In Proceedings of the 35th Euroean Conference on IR Research, 7814: Sringer Berlin Heidelberg. doi: / _20 Jadidinejad, A. H., & Mahmoudi, F. (2014). Advertising Keyword Suggestion Using Relevance-Based Language Models from Wikiedia Rich Articles. Journal of Comuter and Robotics, 5(1): Jeh, G., & Widom, J. (2002). SimRank: a measure of structural-context similarity. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. Joshi, A., & Motwani, R. (2006). Keyword generation for search engine advertising. In Proceedings of the 6th IEEE International Conference on Data Mining Workshos, IEEE. 75

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16 Wu, H., Qiu, G., He, X., Shi, Y., Qu, M., Shen, J., Chen, C. (2009). Advertising keyword generation using active learning. In Proceedings of the 18th International Conference on World Wide Web, ACM. Zhang, Y., Zhang, W., Gao, B., Yuan, X., & Liu, T.-Y. (2014). Bid keyword suggestion in sonsored search based on cometitiveness and relevance. Information Processing and Management, 50(4): doi: /j.im Zhao, P., Han, J., & Sun, Y. (2009). P-Rank: a comrehensive structural similarity measure over information networks. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, ACM. APPENDIX A RESULT OF SENSITIVITY ANALYSIS In this section, we aly sensitivity analysis on arameters used in our model with 0/1 Knasack roblem. The most reason of the analysis is to see how our results would be robust. In deth, we tried to show how much each arameter could be changed as the other arameters and solution were fixed. To roceed sensitivity analysis, first we select three arameters used in our model that are the number of clicks ( cl ), cost-er-click (CPC) ( c ) and Budget ( B ). All the arameters are different to each keyword or model and likely to be changed based on time. As we have done in the exeriment, we fix the time of arameters to revious month ( t 1 ) when they are available to be used in the model and we assume that the arameter remains unchanged in t. And we find the range of how much the variable could be increased or decreased as the other variables and solutions are fixed. Order of keywords in the otimal solution set is also fixed since the order in the solution is an imortant factor in search engine marketing. In the following subsections, we show the results of sensitivity analysis for each arameter. On account of sace considerations, we conduct each analysis for keywords of the five highest scored keywords. A.1.1. Number of clicks ( cl ) Let cl be the number of clicks of can define the maximum increment value of are fixed, we can set the ossible range of th keyword k from the otimal keyword solution set U at time t, cl as cl as follows: cl and the decrement value as 1 U. And we cl. By assuming the other conditions cl Δcl cl Δcl s Δs = s 1, 2 or s Δs = s 1, 1 c c (5) where s is the score and c is the cost of keyword k. It means that each cost can be increased or decreased until the score will not be bigger or smaller than adjacent scores. A.1.2. Cost-er-click ( c ) Let c be the cost er click of decrement value as th keyword k and we can define the maximum increment value of c as c. By adoting all assumtions and notations above, we can set the ossible range of k as follows in Equation (6) with the illustration in Figure 8 (b). c and the c for keyword s cl Δs = s 1, 2 or s Δs = s 1, 1 c Δc c Δc cl (6) 77

17 Figure 8. Illustration of increment and decrement value of arameters in Sensitivity Analysis: (a) Budget, (b) Number of clicks and cost-er-click In the result Table 6, 7 and 8, we can find that the increment or decrement value for each keyword is different. The range of value is deendent on the score value of right rior or osterior keyword in the order. If the ga between the score of rior and osterior of target keywords is quite big, the target needs to be changed while other conditions and solutions are not changed. In contrast, the solution set can be easily changed based on the changes of each arameter when the scores of keywords are similar to each other. So based on the result of sensitivity analysis, advertisers will exect how much the otimal keyword solution will be robust when the number of clicks and CPC for each keyword are changed in time t. A.1.3. Budgets ( B ) Though budgets available for search engine marketing at time t are diversified in our result, each budget can be slightly changed with not changing other variables and otimal solutions. Figure 8 (a) shows how much each budget had its ossible range of value by letting the maximum increment value of B as B and decrement value as B. And it can be converted to the equation as follows: B ΔB U ck c U 1, or B ΔB c k= 1 U k k= 1 (7) Table 6. Result of Sensitivity Analysis for the number of clicks and CPC (Won): Songwol towel Keywords Avg. # of clicks in t-1 Avg. CPC in t-1 Exected costs in t Re-skin towel event , Inf Bamboo Bebe , Steam Towel , Return resents , Towel for Coule , Keywords Score Table 7. Result of Sensitivity Analysis for the number of clicks and CPC (Won): Imlant Avg. # of clicks in t-1 Avg. CPC in t-1 Exected costs in t O-lant Dental Clinic , Inf Miso-lant Dental Clinic , Insurance for Teeth , Oslo Dental Clinic , Solid Dental Clinic , Score cl cl cl cl c c c c