A PREDICTIVE MODEL FOR CUSTOMER PURCHASE BEHAVIOR IN E-COMMERCE CONTEXT

Transcription

1 A PREDICTIVE MODEL FOR CUSTOMER PURCHASE BEHAVIOR IN E-COMMERCE CONTEXT Jangtao Qu, School of Economc Informaton Engneerng, Southwestern Unversty of Fnance and Economcs, Chengdu, Schuan, Chna, Abstract Predctng customer purchase behavour s an nterestng and challengng task. In e-commerce context, to tackle the challenge wll confront a lot of new problems dfferent from those n tradtonal busness. Ths study nvestgates three factors that affect purchasng decson-makng of customers n onlne shoppng: the needs of customers, the popularty of products and the preference of the customers. Furthermore, explotng purchase data and ratngs of products n the e-commerce webste, we propose methods to quantfy the strength of these factors: (1) usng assocatons between products to predct the needs of customers; (2) combnng collaboratve flterng and a herarchcal Bayesan dscrete choce model to learn preference of customers; (3) buldng a support vector regresson based model, called Heat model, to calculate the popularty of products; (4) developng a crowdsourcng approach based expermental platform to generate tran set for learnng Heat model. Combnng these factors, a model, called COREL, s proposed to make purchase behavour predcton for customers. Submtted a purchased product of a customer, the model can return top n the most possble purchased products of the customer n future. Experments show that these factors play key roles n predctve model and COREL can greatly outperform the baselne methods. Keywords: predctve model, purchase behavor, E-commerce

2 1 INTRODUCTION If a frm s able to predct customers purchase behavor, the frm wll really beneft much from ths ablty, such as mprovng success rate of acqurng customer, ncreasng sales and establshng compettveness. There have been researchers from marketng and customer relatonshp management (CRM) makng ther contrbutons for the purchase behavor predcton. Market basket analyss [Raymond et.al. 2005, Shu et.al. 2011] examnes purchase lsts of supermarket or shop to dentfy purchase pattern. Wth the pattern, the needs of one customer can be predcted. Scholars n CRM employ technques of data mnng to evaluate customers value [E. H. Suh et.al. 1999, J. R. Bult et. al. 1995, J. A. McCarty et. al.2007], helpng frms to acqure customers or mprove customer retenton. Dfferent from tradtonal busness, enterprses n e-commerce have no way to acqure nformaton about customer demography, geography and famly background. Instead, t s convenent to obtan revews, ratngs of product and vstng tracks. Therefore, those methods and algorthms for customer purchase behavor predcton n tradtonal busness are not sutable for e-commerce context. Is t probable to predct customer purchase behavor n e-commerce context? Anand [Anand 2008] suggests that purchases are vewed as two dstnct types n onlne shoppng: frmntated purchase, whch s a consequence of the frm makng recommendatons, and others apart from the frm-ntated purchase. In our opnon, the second type may be further dvded nto self-ntated purchase and assocaton-ntated purchase. The self-ntated purchase ndcates that current purchase s not related wth past purchase behavor whle the assocaton-ntated purchase consders t to be assocated wth prevous experence. Let s envsage a scenaro: a begnner photographer bought a camera; wth mprovement of hs photography sklls, a trpod can possbly emerge on hs schedule, and then a remote shutter wll also probably follow t. As a tool for frm-ntated purchase, recommender system s a technology adopted wdely n e- commerce webstes. Recommender system [Lnyuan et.al. 2012, D. Jannach et.al. 2011] predcts whch tems a customer wll most probably lke or be nterested n va ether explotng ratngs made by customers wth smlar taste (collaboratve flterng) or usng ratngs of the customer for other products n past tme (content-based recommendaton). However, recommender system generally predcts a ratng for a canddate product that only represents what mpresson the customer wll have for the product. The ratng s far from predctng purchase behavor of customers. Accordng to our experments, usng collaboratve flterng as a tool for predctng customer purchase behavor leads to a very poor performance. To predct the self-ntated purchase s an extremely dffcult task snce too many factors can actvate a customer s purchase desre whle many of them cannot be acqured n the onlne context. For example, a man may go to buy a TV set due to old one was out of order. The desre of customer s almost mpossble to be predcted n e-commerce context. Referrng to the assocaton-ntated purchase, however, past purchased products of customers may reveal ther needs. Hence, t s vable for predctng customer needs va explorng assocatons among products. From our pont of vew, customers purchase behavors generally are motvated by ther needs. Addtonally, the popularty of products has an mpact on the purchasng decson-makng of a customer. For example, a product n whch no one has shown any nterest s dffcult to actvate a customer s desre. Also, customers preference for products plays key roles for ther purchasng decson-makng. Ths study focuses on the assocaton-ntated purchase predcton n e-commerce context. We propose a predctve model ncorporatng customer needs, the popularty of products and customer preference, whch are usually gnored n prevous works. When a customer submts one purchased product nto ths model, t can return top n the most probably purchased products by the customer n future.

3 We explore assocatons between products, explotng them to predct customer needs. The popularty of a product s subect to many factors such as on-shelf date, recent revew date, ratngs and the number of revews. We develop a Support Vector Regresson (SVR) based model, called Heat model, to calculate popularty of products. However, we fal to fnd out tran dataset for Heat model from e- commerce webstes. Accordng to our experence, customers are generally able to make a correct udgment on the popularty of a product when they are dong an onlne shoppng. Relyng on crowdsourcng approach, we develop an expermental platform n whch partcpants gve a comparson on the popularty of a par of products. The system converts the choces of partcpants to nstances of tran data for learnng the parameters of Heat model. The dscrete choce analyss s usually used to buld the behavoral model for customers decson-makng. We also combne collaboratve flterng and a herarchcal Bayesan dscrete choce model to learn customers preference for products. 2 RELATED WORK Researchers n marketng and retalng felds have pad ther efforts on predctng customers purchase behavor, whch may help frms to mplement cross sellng and up sellng campagns. Market basket analyss or Assocaton Rule s a man technque for the predcton. They fnd customer purchasng patterns by extractng assocatons or co-occurrences from stores transactonal databases. [Raymond et.al. 2005, Shu et.al. 2011]. CRM system of a frm generally needs to mantan a large amount of customer data, such as age, gender, ncome and purchasng lsts. Data mnng technques are often employed n analytcal CRM to transform ths large amount of data nto valuable knowledge that can be used to support marketng decson makng. Based on such data mnng technques, customers may be segmented nto clusters wth nternally homogenous and mutually heterogeneous characterstcs [Chhl et.al. 2008]. Besdes segmentaton, customers can also be ranked on ther probablty to behave n a certan way (e.g. buyng a specfc product or respondng to a certan marketng campagn). Wth helps of these segmentaton schemes and rankngs, a frm s able to approach carefully selected customers, resultng n a hgher success rate of ther marketng campagns [E. H. Suh et.al. 1999]. Consequently, researchers often try to mprove CRM by enhancng the data mnng technques themselves. Researches n CRM have evolved from RFM models (.e., recency, frequency and monetary value of customer purchases) to classfcaton technques such as ch-square automatc nteracton detecton (CHAID) and regresson models [J. R. Bult et.al. 1995, J. A. McCarty et. al. 2007]. Recently, researchers try to outperform these prmtve technques by ntroducng more advanced machne learnng algorthms, lke support vector machnes, neural networks and random forests [H. Shn et.al. 2006, J. Zahav et. al. 1997]. In general, t s dffcult to acqure customer demography and prvacy nformaton such as ncome, age and gender n e-commerce context. Instead, t s easer to acqure web accessng data such as product revews and ratngs, whch provde rcher nformaton than tradtonal CRM. Therefore, those predcton technques n CRM based on customer data cannot be well transferred to e-commerce doman. There have been few researches focusng on predctng purchasng behavor n e-commerce context or onlne shoppng. [Poel et.al. 2005] employ logt modelng to predct whether or not a purchase wll be made durng the next vst to the webste. The model uses clck steam behavor, customer demography and hstorc purchase behavor as varables. Ther study shows that clck stream behavor s mportant when determnng the tendency to buy. E-commerce frms generally use recommender system for predctng customer purchase behavor. However, as dscussed n Secton 1, recommender system that generally calculate a ratng for a canddate product only predct what mpresson the customer wll have for the product, whch s far dfferent from predctng purchase behavors of the customer. There have been many excellent surveys [Lnyuan et.al. 2012, D. Jannach et.al. 2011] about recommendaton system. We wll not gve a detaled dscusson about t n ths paper.

4 3 CUSTOMER PURCHASE BEHAVIOR PREDICTIVE MODEL Ths secton proposes a customer purchase behavor predcton model COREL (CustOmer purchase predcton model). Let c k be a customer; d and d be products. When c k purchased d n tme t, COREL can return top n the most lkely purchased products by c k after t tme. As c k bought d n t tme, the probablty that c k wll also purchase d after t tme may be p( d ck, d ) p( d, ck ) p( d ck, d) p( d, c ) Suppose c k and d are ndependent of each other,.e. c k may purchase any product n tme t, the probablty may be p( d d) p( d ck ) p( d ck, d) pd ( ) where p(d c k ) s the probablty that c k wll purchase d. p(d d ) s the probablty that a customer bought d wll also purchase d. p(d ) s pror probablty of d. Let ω={d 1,,d -1,d +1,,d m } be a collecton of canddate products. We calculate p(d c k, d ) for each product d ω, and then rank them. When the pror probablty of a product p(d ) s assumed to be unform across all products, p(d ) can be gnored. Therefore COREL may be p( d c, d ) p( d c ) p( d d ) k k COREL can be understood as a two-stage approach: usng p(d d ) to buld a product collecton ω where products are assocated wth d ; usng p(d c k ) to pck up the most lkely beng purchased canddates from ω. Therefore, the most mportant task for buldng a model COREL s estmatng both parameters p(d d ) and p(d c k ). 3.1 Estmatng p(d d ) k Parameter p(d d ) represents the probablty that d wll also be purchased by the same customer after d s purchased. The parameter can be estmated by explorng assocaton between d and d, whch may be calculated va market basket analyss. When both products occur n same one market basket, t s generally thought that there exsts an assocaton between both products. Usng maxmum lkelhood estmaton, d d p( d d) d d denotes the number that product d s purchased; d d s the frequency of both products d and d co-occurrng n the one market basket. However, experment shows that the collecton of canddates bult usng formula (1) s so small that COREL fal to acheve a good performance. Therefore, we suggest buldng assocaton of category, and then pckng up canddates from assocated category of a product. Generally, e-commerce webstes assgn ther products the multlevels categores. For nstance, ngdong ( has three level categores for ther products. For one tem EPSON LQ-630k Prnter, ts categores from frst level to thrd level s Computer or Offce Equpment->Prntng related Offce Equpment->Prnter. We generate categores assocaton n thrd level of categores. Thr(d ) denotes the thrd category of product d. So, (1) Thr( d) Thr( d ) p( d d) Thr( d ) (2)

5 Experments reported about n secton 4 demonstrate that assocatons of categores can extend the canddate collecton. COREL acheve a better performance by pckng up top n assocated categores than that of usng formula (1). 3.2 Estmatng p(d c k ) Parameter p(d c k ) ndcates the probablty that customer c k wll purchase product d. However, t s almost mpossble to accurately estmate the parameter. Accordng to our experences of onlneshoppng, we may fnd that the parameter s subect to two factors: the popularty of d and preference of c k. Suppose both factors are ndependent of each other, formally, p(d c k ) s approxmately computed wth formula (3) p( d c ) Hot d * Preference c, d k k It s beleved that a product that s purchased much more frequently and has hgher ratngs than others wll be more popular n customers purchase decson-makng. Hence we develop a model, called Heat model Hot(d ), to calculate the popularty of products. Customer s preference also plays an mportant factor durng hs/her purchase decson- makng. We propose an approach to learn customers preference of products Preference(c k,d ), whch s presented n Secton Heat Model (3) Besdes the needs of a customer, the number of revews and ratngs of a product also play mport roles on purchase decson-makng of the customer. If a product s gven a lot of low ratngs or sparse revews, t s beleved that a customer wll hestate to purchase the product even f he has a strong need for t. We explot revews and sale nformaton of a product to calculate ts popularty. They nclude (1) Qr, the number of revews; (2) Qs, the average of ratngs; (3) Qa, the number of days snce on-shelf; (4) Qu, the number of days snce recent revew. We use a vector to represent the product d n whch there are four elements (Qr, Qs, Qa, Qu). Prevous researches have shown that SVR (Support Vector Regresson) s an excellent tool for predctve tasks. We develop a SVR based model to calculate popularty of products, called Heat model Hot(d ). Gven a product wth ts four attrbutes Qr, Qs, Qa, Qu, Heat model can calculate a score for ts popularty. The tran set s a necessary component for learnng a Heat model. As far as we know, none of the e-commerce webstes provdes labeled data about popularty of products. However, we can observer that a vstor can possbly decde whch one of two products s more popular n onlne shoppng. Based on the observaton, we use followng four steps to generate a Heat Model. Step 1: Reled on crowdsourcng approach, we develop a platform n whch partcpants need to pck up a more popular product from a par of products dsplayed n web page. The nterface of the system shows n Fgure 1. The system works wth followng steps. Pcks up any two products A and B from products database; dsplays factors Qr, Qs, Qa and Qu of both products n web page; a partcpant chooses a more popular one from them; If Hot(A)>Hot(B), generates two nstances of tran set. Err_Qr Err_Qs Err_Qa Err_Qu label Qr(A)- Qr(B) Qs(A) -Qs(B) Qa(A)- Qa(B) Qu(A)- Qu(B) 1 Qr(B)- Qr(A) Qs(B) -Qs(A) Qa(B)- Qa(A) Qu(B)- Qu(A) -1 In the obtaned tran set, one nstance ncludes fve felds: Err_Qr, Err_Qs, Err_Qa, Err_Qu and label. Qr(A) denotes the element Qr of vector A.

6 Fgure 1. A crowdsourceng approach based expermental platform Step 2: We buld a logstc regresson model f(φ) that may be able to compare the popularty of two products. φ n the model s a vector where the elements, denoted as Err_Qr, Err_Qs, Err_Qa, Err_Qu, represent dfference between elements of both compared product vectors. Where exp( ( )) f ( ) 1 exp( ( )) ( ) Err _ Qr Err _ Qs Err _ Qa Err _ Qu We employ tran set obtaned n step 1 to tran the logstc regresson model. Step 3: An algorthm usng logstc regresson model f(φ) to calculate popularty of products s descrbed as follows. Algorthm 1. Calculate Popularty of Products Input: a collecton of products ω, logstc regresson model f(φ) Output: the popularty of products n ω steps: 1. P [] 2. For each par <a,b>, a,b ω, a b 3. φ =V(a)-V(b) 4. score=f(φ) 6. P[a]= P[a]+score-0.5; P[b]= P[b]+0.5-score 9. End 10. normalze P to range [0,1] 11. Return P In the algorthm 1, the array P stores the calculated popularty of all products n ω n range of [0, 1]. Step 4: Usng algorthm 1, we calculate popularty for each products n set ω, and further generate a tran set for SVR model. Two nstances n the tran set are shown n followng table where score refers to popularty of a product and Ln(Qr) s natural log of Qr attrbute. Ln(Qr) Qs Ln(Qa) Ln(Qu) score In ths study, we explore -SVR and μ-svr combnng wth the polynomal kernel and the radal bass functon that are used as the kernel functon of SVR respectvely. Snce there s few general gudance to determne the parameters of SVR, ths study vares the parameters to select optmal values for the

7 best predcton performance. Expermental results show that -SVR wth radal bass functon can reach best performance n our study. Ths study uses LIBSVM software system [Chh et.al. 2011] to perform experments. Gven a set of data ponts, {(X 1,z 1 ),,(X m,z m )}, such that X R n s an nput and z R 1 s a target, the standard form of -SVR s Subect to mn wb,,, * l 1 T w w C C T W ( X ) b z z W ( X ) b l T * *, 0, 1,..., When Heat model reaches the best performance, the parameters of -SVR are C=1 and = Learnng Customer Preference Economc models of choce typcally assume that an ndvdual s latent utlty s a functon of brand and attrbute preference [Sha et.al. 2003]. Collaboratve flterng (CF) may be used to estmate a customer s ratng for one product n e-commerce va explotng the product ratngs made by customers wth smlar taste. However, CF doesn t consder a customer s preference for prce and brand of products that play an mportant role n customer s purchase decson makng. We predct the c k s ratng for d usng collaboratve flterng, CF(c k,d ), and then propose a herarchcal Bayesan dscrete choce model to learnng preference of customers to prce and brand, DC(c k,d ). CF(c k,d )*DC(c k,d ) refer to preference of customer c k to product d. CF(c k,d ) s calculated n formula(4). Sm( ck, s) ratng( s, d ) ss CF( ck, d ) S (4) where S denotes a set of customers that conssts of top 10 the most smlar customers wth c k ; ratng(s,d ) refers to a ratng that customer s make for product d. The possble ratng values are defned on a numercal scale from 0 (strongly dslke) to 5 (strongly lke). Sm(c k, s) ndcates the smlarty between customers c k and s, whch can be calculated by usng cosne measure. A customer feature vector s defned as a set of ratngs of products. For example, the feature vector of c k, V(c k )=(0, 4, 1, 0, 5) represents that c k dd not purchase product d 1 (or he/she gve a 0 ratng value) and gave d 2 a ratng value 4, etc. l * V ( ck) V ( cl) Sm( ck, cl) V ( ck) V ( cl) (5) Experments reported about n secton 4.3 analyze how CF mpact on performance of COREL. Expermental results show that a model combnng p(d d) wth CF can outperform the basc models usng only ether p(d d ) or CF on predctng customer purchasng behavor. We propose a herarchcal Bayesan dscrete choce model to learn customer c k s preference for prce and brand. Employng the model, we can calculate to what extent customer c k prefer a product d, DC(c k, d ). We dvde prce and brand of every product nto three levels respectvely: hgh, medum and low prce; large, moderate and small brand. In ths way, the feature vector x of a product d has sx bnary value features x = (p_h, p_me, p_lo, b_la, b_mo, b_sm) correspondng to three prce levels and three brand

8 levels, respectvely. Only one of three prce levels n the feature vector has a value 1 whle others s 0. For example, (p_h=1, p_me=0, p_lo=0) ndcates the prce of a product s n hgh level. Brand features are also subected to the rule. For example, (b_la=0, b_mo =0, b_sm=1) means that a product belongs to the small brand. 1 DC( ck, d ) P( y 1) 1 exp( V ( d, ck)) (6) Utlty functon,, U d c V d c e k k k k V d, c p _ h p _ me p _ lo b _ la b _ mo b _ sm P(y =1) denotes the probablty of selectng product d. Sx coeffcents 1 ~ 6 n the utlty functon are decded by features of customer. It means that each customer can face the utlty functon wth dfferent coeffcents. We use followng features to construct a customer feature vector. R (Recency): the number of months that have passed snce the customer last purchased F (Frequency): number of purchases n the last 12 months. M (Monetary): the amount of value from the customer n the last 12 months. Sd, whch s standard devaton of prces of total purchase products of the customer. The value reveals the habt of onlne shoppng of the customer. The smaller Sd means that the customer lke purchase fxed varety of product whle larger Sd ndcates that the customer don t mnd prce of products n onlne shoppng. Age, whch denotes tme nterval n year from current date to date when the customer frst purchase product n the webste. Every customer may have hs preference for products prce and brand. For example, someone prefers large brand products whle other ones do not care a products brand n condton that t s cheap. In the herarchcal Bayesan model, the coeffcents n utlty functon are decded by customers features. Use B denotes 1 ~ 6. B Z U; u ~ N(0, V ) The matrx Z contans features of customers. The coeffcent matrx has a normal dstrbuton wth means vec( ) and covarance matrces gven by Kronecker product of A -1 and V. n ~ N( ' Zn, V ) vec N vec A V 1 ( ) ~ ( ( ), ) V ~ IW ( v, V ) The vec operator creates a column vector from a matrx by stackng the column vectors of [Jan et.al. 2007]. Hyperparameter V has an Inverted Wshart pror. We set nonnformatve pror v, V, and A to v=m+3, V v I, =0 and A=0.01 where m s the number of coeffcents n utlty functon. Parameters n the herarchcal Bayesan model can be descrbed usng a DAG (Drected Acyclc Graph) n Fgure 2.

9 v, V V A, y Fgure 2 DAG of parameters n the herarchcal Bayesan model We employ MCMC-metropols hastng algorthm to estmate parameters n the herarchcal Bayesan model, usng a normal dstrbuton as the proposal dstrbuton for the MCMC algorthm. The loglkelhood functon s L( X, Y, B) log( p( x )* y (1 p( x ))*(1 y )) exp( x * B) px ( ) 1 exp( x * B) The steps for estmatng parameters of model are as follows. Algorthm 2. Use MCMC-metropols hastng algorthm to estmate parameters steps: 1. ntatng old 2. draw from V v,v~ IW(v+n,V+S) 1 vec( ), A, V ~ N( vec( ), A V) 3. draw from 4. draw new ~ N( old, V ) 5. Compute ( old, new) ~ mn(1, p( new) q( new, old ) / p( old ) q( old, new)) Where p( new) / p( old ) exp( L( X, Y, new) L( X, Y, old )), q(, ) / q(, ) exp{( ' Z)* V *( ( Z)') ( ' Z)* V *( ( Z)'))} new old old new new new old old 6. If <1 then 7. old = new wth probablty 8. else 9. old = new 10. End 11. Goto step (2) untl loop end Usng saved draws, we can plot posteror dstrbuton of coeffcents. Fgure 3 llustrates posteror dstrbutons of three coeffcent p_h, p_me and p_lo for one customer. It can be observed that means of three dstrbuton s about -2.7, 0.8 and 0.5, respectvely. From pont estmate of three coeffcents, we can conclude that the customer generally reects hgh prce product and tends to prefer medum prce products to low prce one. The traned model reveals more nformaton about customers. Fgure3. Posteror dstrbuton of coeffcents of a customer To learn a herarchcal Bayesan dscrete choce model, t s necessary to know the choces of customers n a fnte alternatve set. In e-commerce context, however, we can only know what customers purchased, not to know what customer gave up n ther choce. When tranng a herarchcal

10 Bayesan dscrete choce model, both postve and negatve samples are necessary components. Regardng the purchased products as postve data, we develop a technque to generate one negatve nstance from the postve one. One nstance n tran dataset s a feature vector of a purchased product combned wth a label. Sx features p_h, p_me, p_lo, b_la, b_mo, b_sm n an nstance represent prce level and brand level of a product, respectvely. p_h p_me p_lo b_la b_mo b_sm label When each feature n the postve nstance s nverted, we can derve a negatve nstance. p_h p_me p_lo b_la b_mo b_sm label EXPERIMENTS 4.1 Dataset Jngdong s a well-known B2C e-commerce webste n P.R.Chna. We collected customer nformaton and revews of products from the webste. The collected data contan 727,878 product tems, 342,451 customers and 14,634,059 revews from 2004 to 31 January, The products n Jngdong are assgned three level categores. There are 19 frst-level categores, 124 second-level categores and 1078 thrd-level categores. It s dffcult to drectly collect purchase data of customers from e-commerce webste as they are generally regarded as prvacy. Our study s based on an assumpton: f a customer frequently wrtes revews n an e-commerce webste, hs revews can almost totally exhbt purchased products of the customer (n Jngdong webste, only customers who purchased a product s authorzed to wrte revew for the product). Therefore, we pck up customers wth hgh revewng frequency to generate purchase data from ther revews. In addton, there are 55 partcpants usng our crowdsourcng platform to generate tran data that have 1351*2 nstances. The collected data are processed as follows. (1)Dvdng dataset: Dvde the dataset nto three sectons by date. A secton: before 30th June, 2012; B secton : from 30th June, 2012 to 31th July,2012; C secton: after 31th July, (2)Pckng up customers: We pck up customers who wrote revews n all three sectons and the number of revews n A secton s more than 30. There are total of 2770 customers meetng the requrement. (3)Test set: those products that are revewed n C secton by the pcked customers. (4)Tran set: purchased data of the pcked customer n A secton. (5)Target set: C secton (6)Settng prce level for every product: Let d be a product, thr be ts thrd-level category. If the prce of d s up to 75th percentle of prce of all products n thr, we assgn features of d to p_h=1, p_me=0 and p_lo=0. If the prce of d s less than 25th percentle of prce, the features s p_h=0, p_me=0 and p_lo=1. Otherwse, p_h=0, p_me=1 and p_lo=0 (7)Settng brand level for every product: we examne the dstrbuton of the number of product tems for all brands. If the number of product tems of a brand s larger than 75th percentle of the dstrbuton, we set features all products belongng to the brand as b_la=1, b_mo=0 and b_sm=0. If the brand s less than 25th percentle of the dstrbuton, the feature s b_h=0, b_mo=0 and b_sm=1. Otherwse, b_h=0, b_mo=1 and b_sm=0 We call the processed data as JD dataset.

11 4.2 Expermental Results Ths secton nvestgates performance of COREL on predctng customer purchase behavor. We compare COREL to several baselne methods. These models are shown n Table 1. name model descrpton M1 p(d d ) Usng formula (1) to pck up assocated products M2 p(d d )*CF Usng formula(2) to buld canddate set and employng collaboratve flterng to pck up products from canddates M3 p(d d )*CF*Hot A model combng M2 wth Heat model M4 COREL Incorporatng customers prce and brand preference Table 1 Predcton model Four models n Table 1 make predctons on JD data set. These models use the latest purchased product d n B secton by customer c k to predct purchased ones by c k n C secton. The canddates are generated by pckng up ten most assocated categores of d. These models calculate a predcton score for all canddates, and pck up top n canddates to buld a product subset ω. If any product n ω occurs n test set Φ, we call customer c k s successfully predcted. Usng precson as a measure, we present the expermental result n Table 2. Table 2 Evaluate predcton performance for four models Table 2 shows that market basket analyss (M1) has a poorest performance on predctng purchasng behavor of customers. Combng collaboratve flterng and market basket analyss (M2), predctve model can dramatcally mprove ts performance. When combnng M2 and Heat model, the predctve performance can be further ncreased. The model M4 ncorporates prce and brand preference of customers. We can see brand and prce preference do not make a sgnfcant mprovement for predcton performance when n=10. But M4 outperform other three models when n=3 and n=1. It means that the proposed model COREL n ths study s feasble and effectve on predctng customers purchase behavor. 5 CONCLUSION n M1 M2 M3 M % 9.9% 11.4% 15.8% 3 6.8% 13.1% 15.7% 17.5% 5 8.9% 19.9% 23.5% 23.5% % 27.7% 32.9% 32.2% Researchers from marketng and CRM felds make a lot of sgnfcant contrbutons on customer purchase behavor predcton for tradtonal busness. However, n e-commerce context, new methods and technques need to be developed to deal wth the predctve problem. In ths study, we nvestgate several key factors that have an mpact on purchasng decson-makng of customers n e-commerce context, ncludng the needs of customers, the popularty of products and the preference of customers. Furthermore, explotng purchase data and ratngs of products, we propose methods to quantfy the strength of these factors. We beleve that there exst assocaton-ntated purchase n on-lne shoppng and t can be exploted to predct the needs of customers. Experments n ths study favor our pont of vew. It s reasonable that we dvde onlne shoppng to three types: frm-ntated purchase, self-ntated purchase and assocaton-ntated purchase. Accordngly, experments also show that assocatons between categores of products can sgnfcantly mprove the predctve performance.

12 We develop a SVR based model, called Heat model, to calculate the popularty of products. However, none of the e-commerce webste provdes the labeled data for tranng the model. Relyng on crowdsourcng approach, we develop a system that can generate tran set va collectng partcpant s udgment on popularty of products. Experments prove that our approach s feasble and popularty of products s also a key factor mpactng on-lne shoppng. We also combne CF and a herarchcal Bayesan dscrete choce model to learn preference of customers. Experments demonstrate that customers preference play an mportant role on the purchase decson-makng of customer. The model COREL, whch combnes products assocatons, the popularty, and customer preference, may be appled to predct the most possble purchased products of a customer. Experments show that COREL obvously outperform the baselne methods. Acknowledgements Ths work was supported by Maor Program of Natonal Natural Scence Foundaton of Chna (No ) and the Fundamental Research Funds for the Central Unversty of Chna (No. JBK120505). References Anand V. Bodapat (2008). Recommendaton Systems wth Purchases Data, ournal of marketng research, 45 (1), Chhl Hung and Chh-Fong Tsa (2008). Market segmentaton based on herarchcal self-organzng map for markets of multmeda on demand, Expert System wth Applcaton. 34(1), Chh-Chung Chang and Chh-Jen Ln (2011). LIBSVM : a lbrary for support vector machnes. ACM Transactons on Intellgent Systems and Technology, 27(2), D. Jannach, M. Zanker, A. Felferng, G. Fredrch, (2011). Recommender Systems: An Introducton. Cambrdge Unversty Press, New York. E. H. Suh, K. C. Noh, and C. K. Suh (1999). Customer lst segmentaton usng the combned response model, Expert System wth Applcaton, 17(1), H. Shn, and S. Cho, (2006). Response modelng wth support vector machnes, Expert System wth Applcaton, 30(4), Jan R. Magnus, Henz N. (2007). Matrx Dfferental Calculus wth Applcatons n Statstcs and Econometrcs, JOHN WILEY & SONS, New York. J. A. McCarty, and M. Hastak (2007). Segmentaton approaches n data-mnng: A comparson of RFM, CHAID, and logstc regresson, Journal of Busness Research, 60(3), J. R. Bult, and T. Wansbeek (1995). Optmal selecton for drect mal. Mark. Sc., 14, J. Zahav, and N. Levn, (1997). Applyng neural computng to target marketng. Journal of Drect Marketng, 11(2), Lnyuan Lü, Matúš Medo, Ch Ho Yeung, Y-Cheng Zhang, Z-Ke Zhang, Tao Zhou (2012). Recommender systems, Physcs Reports, 519(1), Poel, D. V. D., & Bucknx, W. (2005). Predctng onlne-purchasng behavor. European Journal of Operatonal Research, 166, Raymond Ch-Wng Wong, Ada Wa-Chee Fu, Ke Wang, (2005). Data Mnng for Inventory Item Selecton wth Cross-Sellng Consderatons. Data Mnng and Knowledge Dscovery, 11 (1), Sha Yang, Greg M. Allenby (2003). Modelng Interdependent Consumer Preferences. Journal of Marketng Research, 40(3), Shu-hsen Lao, Yn-u Chen, Hsn-hua Hseh (2011). Mnng customer knowledge for drect sellng and marketng, Expert Systems wth Applcatons, 38 (5),