Aociation for Information Sytem AIS Electronic Library (AISeL) PACIS 00 Proceeding Pacific Aia Conference on Information Sytem (PACIS) 00 Online Muic Ranking Service: Ranking Mechanim Baed on Popularity and Slot Effect Byungjoon Yoo Seoul Nation Univerity, byoo@nu.ac.kr Kwanoo Kim Seoul Nation Univerity, ku3377@nu.ac.kr Follow thi and additional work at: http://aiel.ainet.org/paci00 Recommended Citation Yoo, Byungjoon and Kim, Kwanoo, "Online Muic Ranking Service: Ranking Mechanim Baed on Popularity and Slot Effect" (00). PACIS 00 Proceeding. 3. http://aiel.ainet.org/paci00/3 Thi material i brought to you by the Pacific Aia Conference on Information Sytem (PACIS) at AIS Electronic Library (AISeL). It ha been accepted for incluion in PACIS 00 Proceeding by an authorized adminitrator of AIS Electronic Library (AISeL). For more information, pleae contact elibrary@ainet.org.
ONLINE MUSIC RANKING SERVICE: RANKING MECHANISM BASED ON POPULARITY AND SLOT EFFECT Byungjoon Yoo, Graduate chool of Buine, Seoul Nation Univerity, Seoul, Korea, E-mail: byoo@nu.ac.kr Kwanoo Kim, Graduate chool of Buine, Seoul Nation Univerity, Seoul, Korea, E-mail: ku3377@nu.ac.kr Abtract Thi paper analyze muic chart of an online muic ditributor. In muic chart, the digital muic provider diplay a daily ranking of t ~ 00th and a weekly ranking of t ~,000th ong on it webite. And the ranking of each ong i aigned baed on treaming volume and download volume. Thi paper tudie how the online muic ditributor hould et it ranking policy to maximize the value of online muic ranking ervice. Compared to the current ranking mechanim which i being ued by muic ite and only conider treaming and download volume, a new ranking mechanim i propoed in thi paper. A key improvement of the new ranking mechanim i to reflect a more accurate preference pertinent to popularity, pricing policy and lot effect baed on exponential decay model for online uer. A ranking model i built to verify correlation between two ervice volume and popularity, pricing policy, and lot effect. An empirical analyi i followed to illutrate ome of the general feature of online muic chart and to validate the aumption ued in the new ranking model. The reult from the empirical work how that the new ranking mechanim propoed will be more effective than the former one in everal apect. Keyword: ranking mechanim, lot effect, exponential decay model 65
INTRODUCTION In day of Internet commerce, ale of recorded muic in compact dik (CD) format have declined teadily becaue conumer increaingly have moved toward digital real time treaming and downloading. The majority of muic i now old in digital format, and real time played or downloaded to online uer audio device uch a mart phone, portable muic player, or other machine. Online muic provider employ a variety of buine model for the ditribution of digital muic. Muic ingle item downloading ervice and ubcription ervice are more and more common a the digital content are delivered more and more by online channel. The characteritic of competition among digital content ditributor, pecifically online muic ditributor, cloely reemble thoe of monopolitic competition. The Internet information reduce earch cot relative to viiting phyical tore (Chevalier and Autan 003). The accurate ranking ervice of muic chart reduce earch cot relative to viiting phyical tore or competitor webite. Online muic ditributor give ome portion of the revenue a commiion to muic ource provider. And they have ranking mechanim to lit the digital content on ranking lot. The digital content ranking i generally decided baed on poible parameter including download volume and treaming volume. A key iue for the online muic ditributor i how it ranking policy hould be determined to maximize the value of the ranking ervice and to maximize it revenue by extenion (Chen 009). The ranking policy in thi paper will be regenerated through the new ranking mechanim, which reflect a more accurate preference pertinent to popularity, pricing policy and lot effect baed on exponential decay model (Breee et al 998) for online uer. In a related mechanim ued in online ponored earch advertiing, earch engine rank by expected revenue (Edelman et al 007). Thi paper repreent an effort to apply a new ranking mechanim to reflect online uer preference and lot effect more accurately compared to exiting ranking ervice of muic chart offered by the online muic provider. For thi purpoe, we ue an analytical method that track how online uer repond the offered ranking ervice with the propoed parameter like popularity and lot effect, and empirical work including a et of generally available tatitic. The uggetion from our analyi on popularity and lot effect will have important economic and ranking policy implication, pecifically a digital content manager try to evaluate the value propoition of the ranking ervice and to maximize the revenue. The paper proceed a follow. Section provide a literature review on the application of ranking information ervice and ranking mechanim for other digital content. Section 3 preent a ranking model of the online muic ditributor and conider how the provider might deign it ranking algorithm to maximize the ranking ervice and it own revenue by extenion. Section 4 preent empirical work including data decription collected from the online muic provider, the illutration of the general feature of online muic chart, and the validation for the aumption ued in the new ranking mechanim model. The lat ection preent concluding remark with ome broader implication and future work. LITERATURE REVIEWS Mechanim deign wa originated from the poibility of efficient reource allocation in ocialit ociety in the 930. And Leonid Hurwicz (960) developed the mechanim deign theory. He defined mechanim a a communication ytem amongt principal and agent, and where a prepecified rule aign an allocation of good and ervice. The incentive-compatibility allowing the incentive of elf-intereted participant i the key notion for mechanim deign theory (Leonid Hurwicz 97). An online uer viiting one of online content ale webite and looking for digital content would typically face a creen the price of the content, the relative ale ranking at the ite, etc (Chevalier and Autan 003). Online uer (agent) pontaneouly acce to muic chart information by ranking mechanim without cot, and they receive a pay-off of reducing earch cot. That i, an allocation through ranking mechanim i realized by voluntary participation of online uer. And the advanced ranking mechanim by deigner (principal) can realize more ophiticated allocation amongt principal and online uer. It reult in the increae of revenue for principal and the 66
increae of utility for agent. In economic perpective, demand of digital content i ignificantly correlated with the relative ale ranking and pricing policy. The ale ranking i likely to timulate the herd intinct of people. The bandwagon effect diturb the theory of upply and demand pertinent to content pricing and individual preference (Leiventein 950). But it i an important phenomenon for the increae of a demand for digital content. In term of ranking effect, Spoerri (008) invetigated whether the rank poition of a document combined with the information of the number of ytem that retrieved it can help to produce a better etimate of the document probability of being relevant. The reult howed that a document probability of being relevant increae a it i placed higher up in a ranked lit, but a document probability of being relevant decreae exponentially a it i located lower down in a ranked lit. It implicate a linkage among digital content placed in a high ranking i.e. popularity. The literature review of ranking relevant work can be categorized into etimated ale of ranking item and ranking mechanim.. Etimated Sale of Ranked Item Bradford (934) etimated the exponentially diminihing return of extending a earch for reference in cience journal. But thi pattern i called a Pareto ditribution in many dicipline. Pareto (896) found that income can be approximated uch a log-linear ditribution, which i a power law probability ditribution that coincide with ocial, cientific, geophyical, actuarial, and many other type of obervable phenomena. Zipf (949) uggeted that city ize follow a log-linear ditribution with a lope of -. It i called a Zipf law, which i mot eaily oberved by plotting the data on a loglog graph, with the axe being log (rank order) and log (frequency). Brynjolfon et al (003) fit data on ale and ale rank to a log-linear (Pareto) ditribution. The ordinary leat quare (OLS) regreion of log-ale on log-rank wa uggeted by Madeline Schnapp of O Reilly Book who reported excellent ucce etimating competitor unit book ale by comparing their ale rank to O Reilly. Chevalier and Goolbee (003) alo fit ale and ale rank data to a lightly different loglinear ditribution with good ucce. And Chen (009) fit download and download ranking data for ifart application to a regreion of log-popularity (download) on rank.. Ranking mechanim In ponored earch, Feng et al (007) examined that the poitive correlation between top placement and increaed traffic create ignificant demand among buinee for top placement on earch engine, epecially for popular and commercially-relevant earch term. And Edelman et al (007) alo examined that earch engine rank advertiement baed on expected revenue, which i the product of expected click and price. Wu and Huberman (008) explore three ranking rule for dynamic aggregation webite. The three ranking rule are novelty, popularity, and expected click defined a the product of pat popularity and a novelty decay factor. It i a ranking mechanim that maximize click over time. And they found that the bet click-maximizing ranking rule depend crucially on the rate decay of novelty. Chen (009) modeled a ranking mechanim baed on popularity and revenue including application price, quality, and ranking. And he arranged the revenuemaximizing ranking rule: ponored earch i baed on revenue, dynamic webite i baed on popularity and novelty and app tore are baed on popularity and revenue. In term of exponential decay model, Breee et al (998) validated that exponential decay of attention i a fairly tandard aumption. Feng et al (007) computed the expected number of click-through for an item at a poition. And their imulation employed an exponentially decaying attention model. For example, actual click-through data obtained from Overtune during 003 for the top five poition acro all affiliate including Yahoo!, MSN, and AltaVita, are fitted extremely well by an exponential decay model (Feng et al 007). 3 RANKING MODEL Thi paper applie and extend exiting ranking mechanim to reflect accurately online uer 67
preference with bandwagon effect, ranking effect and lot effect on muic chart available through online muic ditribution indutrie. For thi tudy, total effect affecting a demand of treaming and download volume i defined. Figure how that the exponential decreae of treaming volume and download volume appear pecifically in a ranking-range of t ~ 00 th. It correpond to the exponential decay of attention aumed by Breee et al (998). It repreent lot effect pertinent to the exponential decay of attention and bandwagon effect and ranking effect baed on the intenive popularity propulion. 000000 Rank 600000 volume 00000 800000 400000 0 00 00 300 400 500 600 700 Download Streaming Figure : treaming & download volume by rank-order of 737 muic-id, 5/07/09~3/08/09 Furthermore, the maive diparity between amount in two ervice in the ame ranking-range indicate that a relative value of each ervice volume hould be etimated differently a the rankingrange goe up or down further. Online muic provider uually employ a buine model including a ingle item downloading ervice ( pu ) and a ubcription ervice ( p ) for the ditribution of digital muic. The pricing policy of ( p u ) for ingle downloading and ( p ) for limited downloading and unlimited treaming i mainly reponible for the maive diparity in a ranking-range of t ~ 00 th. The new model follow ranking rule of online muic ditributor, expected two ervice volume reponding to popularity with bandwagon effect, ranking effect and pricing policy, and the lot effect. The major objective of thi tudy i to find how demand of the two volume reflect the intenive popularity propulion and the lot effect, and which ranking mechanim cater for more valuable information ervice to cutomer. It i believed that, reulting from providing more valuable information ervice, the ervice maximize the revenue for the online muic ditributer. 3. Ranking application Ranking of each ong( r ) ha a demand for treaming volume( ) and a demand for downloading volume ( d ). Streaming count of a ong are calculated by the number of treaming play by online muic litener while download count are calculated by the number of download by online muic down-loader. A ranking i the pair (, d) compoed of a ong demand for volume being treaming played and volume downloaded. The value of one treaming volume i not uually equal to a value of one download volume. The value of a download i regarded a much bigger than that of a treaming play in high ranking ection and the value difference decreae a the ranking lot goe down further. In term of maximizing it revenue, a download i more valuable than a treaming play. The more download volume increae, the more it revenue increae. Additionally, low download volume are high in carcity in comparion with high treaming volume. Therefore, ranking of each ong( r ) i compoed of adjuted treaming volume ( α ) ) and adjuted download volume ( β d). The value parameter of a treaming play i ( α ), and the value parameter of a download i ( β σ ). And d 68
the ummation of the relative ratio ( α, β ) i ( + σ ; σ > ), and ( σ ) i a weight-coefficient d for download. Online muic ditributor have a ranking rule, which put motly more weight-value on α βd download. And they aign a ranking to each ong on ( : ) value bai. Streaming α+ βd α+ βd volume of a ong are eaily affected by popularity ( θ ), reulting in a demand of treaming volume = ( θ). And the demand of treaming volume i convexly increaing in popularity, i.e. ( θ ) > 0 and ( θ ) > 0. Download volume d = d( θ) are alo eaily affected by popularity. θ θ And the demand of download volume i increaing in popularity i.e. d( θ ) > 0 and the marginal θ demand i poitive i.e. d( θ ) > 0. θ A ubcription fee and a ingle unit price aume to be identical acro all ong. In cae of treaming, each ong bear a ubcription fee ( p ), but treaming volume of a ong i not eaily affected by the fee. Each ong i real-time played unlimitedly by a ubcription fee for a et period, = ( p ) reulting in increae of treaming volume in a ubcription fee ( p) > 0. Download volume of p a ong i not eaily affected by a ubcription fee ( p ), but eaily affected by a ingle unit price ( p u ). Each ong can be downloaded up to allocated volume by a ubcription fee, but it tend to be downloaded le than the allocated volume for a et period, d = d( p, pu ) and download volume decreae a a ingle unit price increae, reulting in increae of download volume in ubcription fee, d( p ) > 0, but decreae in a ingle unit price d( pu ) < 0 p p. u Once a ubcription fee i paid, it i a unk cot. Digital muic litener do not concern about additional charge for litening muic for a et period and log-in can download their preference up to the allocated volume during a given period of time. But other people have to conider paying a ingle unit price per a download volume. 3. Demand function The demand Di = Di( i, di) for a given ong () i i a function of treaming volume ( i ) and download volume ( d i ). The demand ( D i ) i the expected volume that ong () i would receive if it i in ranking place i.e., t ~ 5 th, 6 th ~ 0 th, t ~ 50 th, 5 t ~ 00 th, etc. Thi i caled down by the lot effect. The demand ( D i ) increae a popularity ( θ i ) increae i.e. Di( i( θi, p), di( θi, p, pu)) > 0 θi and ( D i ) increae a a ubcription fee ( p ) increae i.e. Di( i( θi, p), di( θi, p, pu)) > 0 p but ( D i ) decreae a a ingle unit price ( p u ) increae i.e. Di( i( θi, p), di( θi, p, pu)) < 0. pu And there are no ubtitutive and complementary relation between ong. Demand function: D = D( ( θ, p ), d ( θ, p, p )) 3.3 Ranking function i i i i i i u 69
Digital muic ditributor aign a ranking to each ong on a demand of two different ervice volume bai. And each ervice value i weight-adjuted baed on the amount of treaming played and downloaded. There may be other parameter uch a age of online muic or artit reputation. In reality, a demand for low-ranked ong i likely to be affected by the other parameter. But thi model concentrate on mixture of treaming volume and download volume a two key factor affecting a ranking core. In particular, the online muic ditributor aign a ranking core to each ong, and then it i rank-diplayed on online muic chart every day and every week. Ranking function: ri( i, di) = Di( αi, βdi) = Di( αi( θi, p), βdi( θi, p, pu)) Let ( r i ) be a ong () i rank, which i baed on it core relative to other core. The parameter ( α, β ) aume to be identical acro all ong in a ectionalized ranking lot i.e. t ~ 5 th, 6 th ~ 0 th, t ~ 50 th, 5 t ~ 00 th, etc. The parameter allow digital muic ditributor adjut the total core baed on rank core by treaming volume and rank core by download volume. That i, ( α ) i a parameter that repreent the relative value of treaming volume compare with the value of download volume. ( β ) i a parameter that repreent the relative value of download volume compare with the value of treaming volume. For a imple example, if a ong ha 390 treaming play and 30 download for a ranking etimation period and ( α ) value i and ( β ) value i 7 390 (.3 ;.3) 30 σ =, total core of the ong i (900 = *390 + 7*30). And the value of download volume (50) i adjuted compare with the value of treaming volume (390) in thi cae. We will explore the condition under which online muic ditributor would prefer certain value of ( σ ) in future work. 3.4 Slot effect Mot ong receive a ranking and it i diplayed on a ranking lot, which i a limited pace on creen. And ranking of a ong can be affected by the lot effect, pecifically in high ranking lot. The lot effect i the phenomenon in which log-in are le likely to liten or download an ong that i further down in the ranking ince they need to expend more effort to croll down to ee thi ong (Chen, 009). For an amplification of ranking lot, the online muic ditributor ha a ranking chart of t ~ 5 th on main page. And the chart can be expanded to t ~ 0 th by clicking an expanion icon. Online muic chart diplay every 50 popular ong in a ectionalized web-page. And uer capture roughly 0 ong by one time crolling down on a wide-creen. Therefore, the lot being applicable to t ~ 50 th ong are approximately categorized into t ~ 5 th, 6 th ~ 0 th, and t ~ 50 th. The lot effect i largely downized a ranking categorie go down further after 50 th. In the literature review, the lot effect ( ρ ) ha often been modeled uing the exponential decay model where lot ( j ) ha it click through rate decreaed by a factor of ρ( j) = for ϕ > (Feng et al, j 007). In thi paper, the lot effect ( ρ ) multiplicatively cale down the demand ( D ), o that the j demand for a ong () i in lot ( φ = : t ~ 5 th j ), ( φ = :6 th ~ 0 th j 3 ), ( φ = : t ~ 50 th j 4 ), ( φ = :5 t ~ 00 th ), j ( φ = :0 th ~), etc i D( (, ), (,, )) ( j i αi θi p βdi θi p pu ρ φ i ). The new ranking model will be built baed on following function (), (), and (3). () Demand function D= D((, θ p), d(, θ p, pu)) () Ranking function ri( i, di) = Di( αi, βdi) = Di( αi( θi, p), βdi( θi, p, pu)) (3) Ranking function (Slot effect) r(, d ) = D( α ( θ, p ), βd ( θ, p, p )) ρ( φ j ) 4 EMPIRICAL WORK ϕ i i i i i i i i u i Thi paper introduce a new ranking model to reflect intenive popularity propulion aociated with 60
the bandwagon effect, which i the phenomenon that people preference for a commodity increae a the number of people buying it increae (Leiventein 950), ranking effect (Spoerri 008) and lot effect. The bandwagon effect eem to be clearly oberved at high-ranked lot even though herd intinct diturb the theory of upply and demand baed on price and peronal preference. Slot effect alo eem to be eminent in high-ranked lot. 4. Data and Hypothee development The raw data for thi analyi obtained from one of top online muic ditributor in Korea. When a uer open the online muic webite, he can view a muic chart of t ~ 5 th in the middle of the web ite, and then other chart by category e.g., a TOP 00 chart, a TOP,000 chart, etc by clicking a muic chart icon. The ranking chart data were gathered tarting from July 5 to Augut 3, 009. And the gathered data were claified into treaming log file, download hitory file, and weekly ranking chart. Available data including login identification (ID), treaming-length, track-id and track-length were minded by uing tructured query language (SQL) from treaming log file. Data uch a loginid, ubcription-bae purchaing or a ingle unit price-bae purchaing were extracted from download hitory file. And data like muic-id, treaming volume, and download volume were gained from weekly ranking chart. The treaming log file provide ueful information: what ong wa played, how many time it wa played, and by whom it wa played in a day. The download hitory file how that who downloaded ong by ubcription fee or by a ingle unit price. And a weekly chart how that what ranking a ong ha and how many treaming and download volume the ong received in a week. The raw data illutrate the exiting ranking mechanim reflecting value meaurement for treaming and downloading relevant ranking ervice. In the following ection, the general feature of online muic chart i decribed. And then, a tight correlation between log (popularity) and ranking, and the lot effect propoed i etimated for a new ranking model. There are three relevant hypothee. Hypothei (The Bandwagon Effect Hypothei): Ranking i poitively correlated with popularity propulion in the highet ranking range. Hypothei (The ubcription fee Hypothei): Ranking of treaming play i poitively correlated with a ubcription fee model in the highet ranking range. Hypothei 3 (The lot effect Hypothei): High Ranking are poitively correlated with lot effect, while low ranking i not poitively correlated with lot effect. 4. Overview of finding about online muic chart Thi ection preent ummary tatitic for overall volume of download, treaming, and mixture by online muic provider. Figure are graph of regreion line and cattered plot of the natural logarithm of popularity (download volume, treaming volume, and adjuted total volume) againt rank. log(download) v. rank log(treaming) v. rank log(total) v. Rank 4 4 0 3 3 9 log(download) 8 7 6 5 log(treaming) 0 9 8 log(total) 0 9 4 7 8 3 0 00 400 600 800 000 00 6 0 00 400 600 800 000 00 7 0 00 400 600 800 000 00 RANK RANK Rank (a) log (download) v. Rank (b) log (treaming) v. Rank (c) log (total) v. Rank Figure : point demontrating relationhip between log (popularity) and rank 6
Rank Download Adjuted Rank Rank Streaming Adjuted Rank Total Rank Adjuted Total 35408 479763 0678 3040 3 46034 95078 5 046 8 5 35704 5 5 690554 6 960 5 6 33505 9 6 654 0 530 6 0 66773 4 0 55543 56 3 66090 8 53490 07 45 708 07 03 805 07 50 08 38 06 08 878 875 08 50 Table : download & treaming and ranking for muic-id, and a weekly muic chart, 3/07/009 AdjutedTotal = c + α(streaming) + β( Download) + ε Variable adjuted total 455.48 *** C (36.8764) 0.995890 *** Streaming (0.0070) 7.04846 *** Download (0.03360) R 0.99987 N 08 ( t ~08 th ) *** p<.00, Standard error are in parenthee Table : ordinary leat quare tet reult of AdjutedTotal againt Streaming and Download Table, how that the ranking of a ong in a weekly muic chart i aigned according to adjuted total. The adjuted total i calculated by a weight-adjuted volume of two ervice i.e. (Download rank: 35408*7: 57%) + (Streaming Rank: 46034*: 43%) = (Total Rank: 0678: 00%). A a reult, the rank of download and treaming i changed into the adjuted rank of download and treaming. The data, gathered for 08 muic-id during the fourth week of July in 009, provide a robut bai for correlating log (popularity) and rank. The variation rate range from 0.5 to 3.6 for log (download), range from 3 to 7 for log (treaming), and range from 4 to 8.5 for log (mixed volume). And rank range from to 08. Summary tatitic are hown in Table 3. Rank log (down-popularity) log (tr-popularity) log (total-popularity) Mean 509.5000 5.78548 8.90797 9.547935 Median 509.5000 5.49768 8.6776 9.97 Maximum 08.000 0.47469 3.0805 3.87593 Minimum.000000 3.637586 6.777647 8.5939 Std. Dev. 94.056.09757.853.047553 Obervation 08 08 08 08 Table 3: ummary tatitic for log (popularity) and rank data Table 4 indicate regreion of log (popularity) on rank. log( popularity) = a+ a. Rank + ε Variable log(down-popularity) log(tr-popularity) log(total-popularity) Rank -0.003303 *** -0.00339 *** -0.003077 *** (5.45E-05) (5.4E-05) (5.64E-05) C 7.46870 *** 0.6354 ***.563 *** (0.03075) (0.03839) (0.03348) R 0.78364 0.794300 0.74580 N 08 ( t ~08 th ) 08 ( t ~08 th ) 08 ( t ~08 th ) *** p<.00, Standard error are in parenthee Table 4: ordinary leat quare tet reult of log (popularity) againt rank 6
The dependent variable are log (down-popularity), log (tr-popularity), and log (total-popularity). The coefficient -0.3% on each rank i the percentage change in popularity for a unit increae in ranking (i.e. from rank to rank ). That i, if ranking of one ong get wore by, it expected popularity will decreae by 0.3%. And every coefficient i ignificant at the 99.9% level. Ranking i an important fact in predicting popularity. The R value of log (down-popularity) and log (trpopularity) how that rank alone explain around 78% of the variation in the log (popularity). But, The R value of log (total-popularity) how that rank alone explain around 74.5%. The power of effect ize wa downized lightly after the mixture of two ervice. Figure how a large variance between regreion line and catted plot in a ranking-range of t ~00 th. It implie there mut be a factor affecting the large variance. And the large variance implicate bandwagon effect, ranking effect and lot effect. 4.3 Analyi of Hypothei (Bandwagon effect) and Hypothei (Subcription fee effect) Figure 3 are graph of reidual plot of the natural logarithm of popularity (download volume, treaming volume, and adjuted total volume) againt rank of t ~ 0 th. 0.5 3. 4.0 3.0 3.8...0 0.0 9.5 9.0...0.8.6.4....0 3.6 3.4 3. 3.0.8 -. -. -. -. 4 6 8 0 4 6 8 0 -. 4 6 8 0 4 6 8 0 -. 4 6 8 0 4 6 8 0 Reidual Actual Fitted Reidual Actual Fitted Reidual Actual Fitted (a) log (download: t ~0 th ) (b) log (treaming: t ~0 th ) (c) log (total: t ~0 th ) Figure 3: reidual point between log (popularity) and rank Variable log(down-popularity) log(tr-popularity) log(total-popularity) Rank ( t ~5 th ) -0.0497 *** (97%) -0.08395 ** (8%) -0.655 *** (95%) Rank (6 th ~0 th ) -0.0488 *** (99%) -0.090 *** (8%) -0.03386 *** Rank ( t ~50 th ) -0.030709 *** -0.0560 *** (93%) -0.00096 *** Rank (5 t ~00 th ) -0.0988 *** (95%) -0.093 *** -0.07783 *** (95%) Rank (0 t ~50 th ) -0.03399 *** -0.00879 *** (97%) -0.00879 *** (93%) *** p<.0, ** p<.05, * p<., R-quared are in parenthee Table 5: OLS tet reult of log (popularity) againt rank in ectionalized lot (not adjuted) Table 5 illutrate log (popularity) on rank in ectionalized lot. The R value of log (tr-popularity) on rank i 8% in a ranking range of t ~0 th. And the coefficient of rank ( t ~ 5 th ) i ignificant at the 5% level not like log (down-popularity). A ubcription fee model implicate a little bit low R value of log (tr-popularity) becaue the treaming ervice i ued unlimitedly with a ubcription fee. On the other hand, the R value of log (down-popularity) maintain a high value of more than 96%. It come from limited downloading ervice. Thi validate hypothei. The coefficient value repreent the intenive popularity propulion in high-ranking lot, i.e. the coefficient -% of log (downpopularity), which indicate that a demand for download volume in a ranking range of t ~5 th will increae up to % a ranking of a ong increae by. The R value of log (tr-popularity) and log (down-popularity) utain 95%~99% in mot ranking lot except the cae. So, hypothei i upported. It i clear that ranking i the mot important factor in predicting popularity. At the ame time, a demand for treaming volume in a high ranking range i affected by a ubcription fee. 63
Variable log(down-popularity) log(tr-popularity) log(total-popularity) Rank ( t ~5 th ) -0.6 ** (83%) -0.0958 (54%) -0.655 *** (95%) Rank (6 th ~0 th ) -0.04984 *** (7%) -0.08974 ** (37%) -0.03386 *** Rank ( t ~50 th ) -0.0935 *** (63%) -0.05580 *** (4%) -0.00096 *** Rank (5 t ~00 th ) -0.06564 *** (43%) -0.07858 *** (47%) -0.07783 *** (95%) Rank (0 t ~50 th ) -0.008947 ** (0%) -0.008 *** (7%) -0.00879 *** (93%) *** p<.0, ** p<.05, * p<., R-quared are in parenthee Table 6: OLS tet reult of log (popularity) againt rank in ectionalized lot (adjuted) The R value of log (total-popularity) maintain a high average around the 95 percent. It reult from a ranking policy, which aign a ranking to each ong on an adjuted value bai by artificial mean without conidering popularity propulion, lot effect, and other factor. Under the exiting ranking policy, table 6 how that the R value of the adjuted log (tr-popularity) how that rank alone explain about 40% ~ 50% of the variation in ranking lot of t ~ 00 th. The R value of the adjuted log (down-popularity) how that rank alone explain 43% ~ 83% percentage of the variation in ranking lot of t ~ 00 th. But the R value teeply decreae a rank drop. 4.4 Analyi of Hypothei 3 (Slot effect) It eem reaonable that if uer need to croll down a couple page to ee an application, they will have a lower likelihood of purchaing the application (Chen 009). Slot ρ =0.4976 Slot ( t ~5 th ) Slot (6 th ~0 th ) Slot 3 ( t ~50 th ) Slot 4 (5 t ~00 th ) Slot 5 (0 t ~50 th ) Download (average) 0 ρ = 7688.6 ρ =0.4976 3778.467 ρ =0.54 66.3667 3 ρ =0.084778 347.38 4 ρ =0.03759 040.8 ρ =0.7 Streaming (average) 0 ρ = 385437.4 ρ =0.700006 69808.5 ρ =0.449777 7336 3 ρ =0.4755 5687.78 4 ρ =0.066678 5700. ρ =0.5887 Total (average) 0 ρ = 85643.6 ρ =0.588736 50404.5 ρ =0.34834 7804. 3 ρ =0.3698 9734.9 4 ρ =0.05393 44000.04 Table 7: lot effect in ectionalized lot Table 7 approximately how that the lot effect i around ( ρ = 0.7 ) for treaming volume. Thi mean a demand for treaming volume in lot i roughly D= D((, θ p ), d(, θ p, p )) ρ( φ ) u t~5th 0 i.e. D *0.7. Demand in lot i D *0.7, and D *0.7 in lot 3. The lot effect i around 0 ( ρ = 0.4976 ) for a demand for download volume. The demand in lot i roughly D *0.4976, D *0.4976 in lot, and D*0.4976 in lot 3. The average demand of treaming and download volume decreae exponentially a the ranking lot goe down. Hypothei 3 i upported. It correpond to the exponential decay of attention aumed by Breee et al (998). 4.5 Poible application of new ranking mechanim Digital content ale compare with their ale rank. The top ranking lot are correlated with exponential decay of attention. A ubcription fee for unlimited uage create ignificant iue for online buine model. The new ranking mechanim we ugget can be applied to other ranking ervice in a variety of online content categorie or in different ervice environment. Online muic ditributor continue a drift of building one-top ervice, in which uer can enjoy treaming play with mart-phone, on the wirele Internet. The mart-phone ha a mall creen diplaying the top 0 64
lit. A muic-id pecific rank in the top 0 lit ha a large effect on treaming volume with a ubcription fee. Determining a ranking of each muic-id for a daily chart or weekly chart, the treaming volume calculated from mart-phone bae reflect the new ranking mechanim very well. Thi mechanim can be applied to real-time harp-increae earch-word i.e. NAVER, No. portal ite in Korea, provide a mall lot diplaying the mot popular 0 lit in real time. By earching the lit of the lot, the ranking of each lit i affected by intenive viibility i.e. bandwagon effect becaue it i very popular, and by lot effect receiving limited concentration. Thu, the lot hould be regarded a carce reource that need to be allocated carefully. Auction.co.kr (a Korean ale webite) provide the bet 00 item of each content category baed on rank core by ale volume of male and rank core by ale volume of female. Thi webite initially how the five bet item and le than 0 recommendation in a price ection. Thu, the ranking of each item in ame price ection can be aigned by the propoed ranking mechanim reflecting bandwagon effect and lot effect in a high ranking range. But the total effect will not be a intenive a the total effect in online muic. 5 CONCLUSION In thi tudy, we have examined the ranking mechanim in online muic ditribution, and have preented a model of a new ranking mechanim where online muic ditributor et pricing policy i.e., a ingle item downloading ervice and a ubcription fee and effort in repone to expected uer demand of downloading volume and treaming volume with intenive popularity propulion i.e., ranking effect and bandwagon effect, and lot effect. The key iue of thi tudy i to deign a ranking mechanim a a communication ytem between online muic provider and online uer. Ranking information derived from the new mechanim provide the allocation of the increae of utility for uer. And it i believed that, reulting from providing more valuable information ervice, the ervice maximize the revenue for the online muic ditributer. A ranking model wa developed to reflect popularity and total effect occurring in high ranking lot. The model ha the pair (, d) compoed of a ong demand for volume being treaming played and volume downloaded. The demand of treaming play i convexly increaing in popularity and reult in the increae of treaming volume in a ubcription fee. The demand of download i alo convexly increaing in popularity and reult in the decreae of treaming volume in a ingle unit price. Thi model concentrate on the mixture of treaming volume and download volume a two key factor affecting a ranking core even though there may be other parameter uch a age of online muic or artit reputation. Ranking lot are ectionalized in a reaonable way Empirical data from one of top online muic ditributor in Korea illutrate ome of general feature of online muic ditribution. Ranking i an important factor in predicting popularity tanding for a demand of download volume and treaming volume. Data preent that popularity decreae by 0.3% due to ranking drop by one tep at 99.9% ignificant level. And the large variance between etimate value of rank and tandard error in a ranking range of t ~ 00 th implicate bandwagon, ranking effect and lot effect in ranking. When table 5 and 6 are compared, the exiting ranking mechanim doe not reflect the real value of download popularity and treaming popularity independently a each R value indicate. Empirical data alo validate aumption propoed in our ranking model. The coefficient value of rank in each ectionalized lot wa downized teeply a the ranking lot go down further at table 5 and 6. The R value, 8%, of teaming popularity in a ranking range of t ~0 th manifet the effect of a ubcription fee employing unlimited ervice at table 5. The lot effect baed on exponential decay of attention i clearly hown in table 7. From our analye, it i expected that the new ranking model will be more effective than the exiting mechanim. Therefore, the new ranking mechanim i recommended when online muic chart are deigned. The introduction of the new model would be helpful for improvement of the exiting ranking mechanim. However, for wider application of thi tudy, application in a variety of online content categorie or in different ervice environment need to be teted with the propoed mechanim. Thu, thi tudy can be extended by future reearch. Analytical model for the propoed mechanim will be built to verify correlation between two ervice volume and popularity, pricing 65
policy, and lot effect. A prefer certain value( σ ) will be explored. While our empirical reult how poitive bandwagon effect and lot effect in a high ranking lot, it may be poible to apply the new ranking model to different ervice environment including mart-phone baed ranking, Internet earch baed ranking, etc. It i alo poible to extend our methodology to analyze ranking mechanim on other online item available through Internet webite. For example, the propoed ranking model can be applied to the online ale of movie, electronic-book, oftware uch a game application, living good available on online auction ite. Reference Breee, J.S., D. Heckerman, C. Kadie. (998). Empirical analyi of predictive algorithm for collaborative filtering. S.M. Gregory F. Cooper, ed., Proceeding of the Fourteenth Conference on Uncertainty in Artificial Intelligence. Univerity of Wiconin Buine School, Madion, Wiconin, 43-5. Brynjolfon, Erik; Yu Hu; Smith, Michael D,. (003). Conumer Surplu in the Digital Economy: Etimating the Value of Increaed Product Variety at Online Bookeller. Management Science, Vol. 49 Iue, p580-596, 7p Chevalier, Judith and Autan, Goolbee. (003). Meauring price and price competition online: Amazon and Barne and Noble. Quantitative Marketing and Economic Chen Yu-Chung. (009). Eay on Mobile Advertiing and Commerce. PhD diertation, Science Technology and Management, Harvard Univerity Edelman, B., and M. Otrovky. (007). Strategic bidder behavior in ponored earch auction. Deciion Support Sytem 43():9-98 Feng, J., H. K. Bhargava, and D. M. Pennock. (007). Implementing ponored earch in web earch engine: Computational evaluation of alternative mechanim. Inform Journal on Computing 9:37-48 Harvey Leibentein, (950). Bandwagon, Snob, and Veblen Effect in the Theory of Conumer Demand. The Quarterly Journal of Economic Hurwicz, L. (960): Optimality and informational efficiency in reource allocation procee. in Arrow, Karlin and Suppe (ed.), Mathematical Method in the Social Science. Stanford Univerity Pre. Hurwicz, L. (97): On informationally decentralized ytem. in Radner and McGuire, Deciion and Organization. North-Holland, Amterdam. Hurwicz, L. (973): The deign of mechanim for reource allocation. American Economic Review 63, Paper and Proceeding, -30. Mookherjee Dilip. (008). The 007 Nobel Memorial Prize in Mechanim Deign Theory. Journal of Economic 0(), 37-60. Spoerri, A. (008). Authority and Ranking Effect in Data Fuion. Journal of the American Society for Information Science and Technology Wu, F., and B. Huberman. (008). Popularity, novelty, and attention. In ACM Conference on Electronic Commerce 66