Application of neural network to classify profitable customers for recommending services in u-commerce

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1 Application of neural network to classify profitable customers for recommending services in u-commerce Young Sung Cho 1, Song Chul Moon 2, and Keun Ho Ryu 1 1. Database and Bioinformatics Laboratory, Computer Science in College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Korea 2. Department of Computer Science, Namseoul University, Cheonan-city, Korea youngscho@empal.com, moon@nsu.ac.kr : moon is correspondence author, khryu@dblab.chungbuk.ac.kr Abstract This paper proposes an application of neural network to classify profitable customers for recommending services in u- Commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, it is necessary for us to classify profitable customers with RFM(Recency, Frequency, and Monetary) score, to use the purchase data to join the customers using SOM network with input vectors of different features, RFM factors in order to do the recommending services in u-commerce, to reduce customers search effort for finding items, and to improve the rate of accuracy. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall. Keywords RFM; Collaborative filtering; SOM(Self-Organizing Map); 1. Introduction Due to the advent of ubiquitous networking environment, it is becoming a part of our common life style that the demands for enjoying the wireless internet using intelligent portable device such as smart phone, are increasing anytime or anyplace without any restriction of time and place. Data mining is useful in finding knowledge from huge amounts of data. Deboeck and Kohonen describe how SOM (Self-Organizing Map) can be used for effective clustering and segmentation of financial data[1]. Clustering algorithm is a kind of customer segmentation methods commonly used in data mining. In this paper, SOM network is applied to segment the purchase data to join user data and finally forms clusters of the purchase data to join user data with different features, RFM factors in order to do the recommending services in u-commerce. The recommendation system helps customers to find easily items and helps the e-commerce companies to set easily their target customer by automated recommending process. Therefore, customers and companies can take some benefit from recommendation system. The possession of intelligent recommendation system is becoming the company's business strategy. A recommendation system using RFM segmentation analysis technique to meet the needs of customers, it has been actually processed the research[2-5]. We can make the solution for an efficient purchase pattern clustering based on SOM. Finally, we can improve the performance of personal ontology recommender system through SOM learning method based on the purchase data to show customer's buying patterns. The next chapter briefly reviews the literature related to studies. The chapter 3 is described a new method for personalized recommendation system in detail, such as system architecture with sub modules, the procedure of processing the recommendation, the algorithm for proposing system. The chapter 4 describes the evaluation of this system in order to prove the criteria of logicality and efficiency through the implementation and the experiment. In chapter 5, finally it is described the conclusion of paper and further research direction. 2. Relative Works 2.1 RFM RFM is generally used in database marketing and direct marketing and has received particular attention in retail. RFM consists of three initial characters. R means recency- How recently a customer has purchased?. F means frequency- How often she purchases?. M means monetary- How much does she spend?. The general way to use RFM model in customer behavior analysis is to sort the customer data by each dimension of RFM variables and then divide the data into five equal quintiles. For recency, the customer database is sorted by purchase dates by descending order. So, the top segment is given a value of 5 and the others are descendingly assigned of 4, 3, 2, and 1. For frequency and monetary, sorting customer visiting frequency data and the customer data related to the amount of the money spent in descending order, respectively. These three variables belong to behavioral variables and can be acted as the segmenting variables by observing customers attitudes toward the product, brand, benefit, or even loyalty from the database. We can suggest that using average purchase amount instead of total accumulated purchase amount is better in order to reduce co-linearity of frequency and monetary. Finally, all customers are presented by 555, 554, 553,, 111, which thus creates 125 (5 5 5) RFM UCAWSN-14, Jeju, Kora, July 7-10, http//

2 cells. Moreover, the best customer segment is 555, while the worst customer segment is 111. Based on the assigned RFM behavior scores, customers can be classified into segments and their profitability can be further analyzed. The RFM score can be a basis factor how to determine purchasing behavior on the internet shopping mall, is helpful to buy the item which they really want by the personalized recommendation[5-6]. 2.2 neural network The SOM introduced by T. Kohonen, is an unsupervised learning algorithm for clustering[8]. Also SOM is called as a neural networks model based on competitive learning. SOM can convert a high dimensional input space into a simpler low dimensional discrete map. It has two layers which are input and feature layers. We can cluster all elements by feature map with two dimensions. Firstly SOM performs clustering with input vector X and weight matrix W. The data point is treated one at a time. Also the closest to is found by Euclidean distance, and then is updated as the following [9]. where and are current and new weights. So moves to. This learning is repeated until given conditions such as change rate of weights and the number of repeat. In this paper, we can use the SOM learning algorithm [9], where and are current and new weights. So moves to. This learning is repeated until given conditions such as change rate of weights and the number of repeat. In this paper, we can use the SOM learning algorithm[9] as the following. Table 1. SOM learning algorithm Input Set of N dimension vector, X // input node Output Subset of input data (M subsets) begin Randomly initialize, for each node ; for (t=0; unless a stopping condition is reached; Increase t) for (for all input data) for (i=0 to M) (1) ; Find the winner j=i such that Di(t) is minimum for over all I ; Update the winner j (and its neighbors) ; end 3. Our proposal for Recommender System in u-commerce 3.1 Clustering Method using SOM based on Purchase data to join user data This clustering using the neural network in this paper had better than clustering the data directly, is depicted. First, a large set of prototyping for clustering user data (much larger than the expected number of output count, purchase pattern in clusters) is formed using the SOM or some vector quantization algorithm. We can apply a SOM clustering to purchase data to join user data in order to classify profitable customers with RFM score having RFM factors for recommending services in u-commerce. Finally the prototyping application is made and the prototyping result is classified to make clusters in order to classify profitable customers with RFM score. The system can use the code of classification(54 bits), demographic variables such as age, gender, an occupation, skin type, region and customer's RFM factors as input vectors for pre-processing so as to be possible to recommend the items with efficiency. The system can make clusters with neighborhood customer-group using a new clustering method, which is classified by the code of classification and customer s RFM score in customer information. The system can take the preprocessing task which is able to use the whole purchase data by each rank of the RFM score and then makes cluster of purchase data sorted by item category, joined cluster of user data called by customer DB, neighborhood user group[5]. As a matter of course, the system can use the whole purchase data(sale). After that, the system using SOM algorithm, can recommend the items by each rank of the RFM score. The SOM learning algorithm for clustering of user s information to join user s score is depicted as the following Table 2. Table 2. SOM learning algorithm for clustering of user s information to join user s score Step 1 Initialize parameters of SOM model // Representative pattern of bits for demographic variable(54bits), RFM(15bits)rs) Step 2 Set input value vector Step 3 Calculate Output value Step 4 Select winner node ) Step 5 Readjust connection weights Step 6 Completion of learning // IF Reach the learning cycles then Make the result of SOM otherwise GO to Step 2 Step 7 Calculate output value Step 8 Calculate winner node (2) (3) (4) UCAWSN-14, Jeju, Kora, July 7-10, http//

3 Step 9 Result of pattern 3.2 The procedural algorithm for recommendation The system can search cluster selected by using the code of classification and customer's RFM score in users' information. It can scan the preference of brand item in cluster, suggest the brand item with the highest score in item category selected by the highest probability for preference of item category as the average of brand item. This system can create the list of recommendation with TOP-N of brand item with the highest score to recommend the item with purchasability efficiently. This system can recommend the items with efficiency, are used to generate the recommendable item according to the basic of loyalty of RFM factors through clustering method using SOM algorithm. It can recommend the associated item to TOP-N of recommending list. This system takes the cross comparison with purchase data in order to avoid the duplicated recommendation which it has ever taken. 4. The environment of implementation and experiment & evaluation 4.1 Experimental data for evaluation We make the implementation for prototyping of e-shopping mall which handles the cosmetics professionally and do experiments. It is the environment of implementation and experiments in Apache2.2.14, j2sdk 1.7.0_11 as Java environment, JSP/PHP as serverside script, JQuery*mobile, XML/XHTML4.0/HTML5.0/CSS3/JAVASCRIPT as client-side script, C#.net framework 2.0, jakarta-tomcat, apache as web server under Windows O.S. 4.2 Experimental data for evaluation We used 319 users who have had the experience to buy items in e-shopping mall, 580 cosmetic items used in current industry, 1600 results of purchase data recommended in order to evaluate the proposing system[4]. In order to do that, we make the implementation for prototyping of the internet shopping mall which handles the cosmetics professionally and do the experiment. We have finished the system implementation about prototyping recommendation system. We d try to carry out the experiments in the same condition with dataset collected in a cosmetic internet shopping mall. It could be evaluated in MAE and output count by RFM score level for the recommendation system in clusters. It could be proved by the experiment through the experiment with learning data set for 12 months, testing data set for 3 months in a cosmetic cyber shopping mall[4]. The 1 st system of clustering method using SOM based on purchase data, is proposing system called by SOM, the 2 nd system is other previous system(k-means) using k-means clustering algorithm based on the whole data. 4.3 SOM Results for application of neural network SOM network is applied to classify purchase data to join the user data, finally forms clusters of purchase pattern groups of user data with different features, demographic variables and RFM factors as input vectors. In order to segment purchase data join to user data into appropriate number of clusters, SOM is applied to determine the number of clusters. Below figure 1 and figure 2, nine clusters are recommended among 1,600 purchase data, 319 customers when recency, frequency, monetary are the three input variables and then divide the data into five equal quintiles beside off demographic variables. From the SOM result, we can find 5 level based on RFM score of customer so as to recommend the items in real-time environment. The following figures show the result with statistics of output counts based on purchase data for the segmentation as comparing SOM and k-means. It is depicted in the result, that level 1 is the RFM score of customer is more 90 points, level 2 is the range of RFM score (score >= 80 and score < 90), level 3 is the range of RFM score (score >= 60 and score < 80), level 4 is the range of RFM score (score >= 40 and score < 60), and level 5 is the range of RFM score (score >=20 and score < 40). The purchase data is not at the range of RFM score (score < 20). It shows the improvement on the number of output purchase pattern count in the result of evaluation levels for the proposal system(som) comparing with previous system(k-means). The proposal is higher on the number of output purchase pattern count than the previous system from level 1 to level 5. As a result of that, the performance of the proposal system is improved better on the number of output purchase pattern count than previous system from level 1 to level 5. Figure 1. descriptive statistics of output counts by the level of RFM score with comparing SOM and k-means 4.4 Experiment & Evaluation The proposing system's overall performance evaluation was performed by dividing the two directions. The first measurement is output counts of purchase pattern in the table 2. The second evaluation is mean absolute error(mae). The mean absolute error between the predicted ratings and the actual ratings of users within the test set. The mean absolute error is computed the following expression (5) over all data sets generated on purchased data. UCAWSN-14, Jeju, Kora, July 7-10, http//

4 MAE = (5) N represents the total number of predictions, ε represents the error of the forecast and actual phase i represents each prediction. Table 3. The result of MAE in cluster by RFM score level RFM SOM K_means SOM k-means Score output output MAE MAE Level count count Figure 2. The result of MAE by comparing proposal system(som) with previous system(k-means) Above table 2 shows the result of evaluation metrics (MAE) for recommendation system. It shows the improvement in the result of evaluation rates for proposal system comparing with previous system(kmeans). The proposal system is better than the previous system from level 2 to level 4 in the part of large purchase count. As a result of that, the performance of the proposal system is improved better than previous system from level 2 to level 4 although it is not good on level 1 and level 5 in the part of small purchase count. The following figure 3 is shown in the site of recommendation of cosmetics on a smart phone. This system can be used immediately in u-commerce under ubiquitous computing environment which is required by real time accessibility and agility because of finishing particular tasks such as clustering and calculating the probability of preference for pre-processing to reduce the processing time. in real-time environment. Figure 3. The site of recommendation of cosmetics 5. Conclusion Recently u-commerce as a application field under ubiquitous computing environment required by real time accessibility and agility, is in the limelight[4]. We proposed an application of neural network to classify profitable customers for recommending services in u-commerce in order to improve the accuracy of recommendation with high purchasability. We have described that the performance of the proposal system with neural network is improved better than previous system(k-means) from level 2 to level 4 in the part of large purchase count although it is not good on level 1 and level 5 in the part of small purchase count. It could make appropriate recommendation for each user's level possible based on neural network in real-time environment. We could simulate the application of neural network to classify profitable customers, generate recommending items to be possible to measure the purchasability for the future.. Thus, we could make the level of RFM score for the measurement of accuracy and efficiency, and validate the system by our results, then we can recommend items by each user's level according to the loyalty of RFM factors. To verify improved better performance of proposing system, we carried out the experiments in the same dataset collected in a cosmetic internet shopping mall. It is meaningful to present an application of neural network to classify profitable customers for recommending services in u-commerce under ubiquitous computing environment which is required by real time accessibility and agility. The following research will be looking for ways of a personalized recommendation using fuzzy clustering method to increase the efficiency and scalability. Acknowledgements This is work 2) was supported by funding of Namseoul University References 1. Deboeck, G. and Kohonen, T. Visual Explorations in Finance with Self-Organizing Maps. London Springer- Verlag Cho, Y.S., Moon, S.C., Jeong, S.P., Oh, I.B., Ryu, K.H. Efficient Purchase Pattern Clustering Based on SOM for Recommender System in u-commerce. Y.-S. Jeong et al. (eds.), Ubiquitous Information Technologies and Applications, Lecture Notes in Electrical Engineering 2014; 280: Cho, Y.S., Moon, S.C., Ryu, K.H. Mining Association Rules using RFM Scoring Method for Personalized u- UCAWSN-14, Jeju, Kora, July 7-10, http//

5 Commerce Recommendation System in emerging data. International Conferences, SecTech, CA, CES3 2012, Held in Conjunction with GST 2012, Communications in Computer and Information Science 2012;341: Cho, Y.S., Moon, S.C., Noh, S.C. Weighted Mining Association Rules Based Quantity Item with RFM Score for Personalized u-commerce Recommendation System. the 8 th International Conference GCP2013, LNCS 2013;7861: Cho, Y.S., Moon, S.C., Jeong, S.P., Oh, I.B., Ryu, K.H. Clustering Method using Item Preference based on RFM for Recommendation System in u-commerce. Ubiquitous Information Technologies and Applications LNEE 2012; 214: Wei, J.-T., Lin, S.-Y. and Wu, H.-H. The review of the application of RFM model. African Journal of Business Management, 2010; 4(19): Kate A. Smith, Jatinder N. D Gupta. Neural Network in Business Techniques and Applications. In The IDEA GROUP PUBLISHING (2001). 8. T. Kohonen, Self-Organizing Maps. Springer T. Hastie, R. Tibshirani, and J. Friedman The Elements of Statistical Learning Data Mining, Inference, and Prediction. Springer UCAWSN-14, Jeju, Kora, July 7-10, http//