A User Selection Method in Advertising System

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1 Int. J. Communcatons, etwork and System Scences, 2010, 3, do: /jcns Publshed Onlne January 2010 ( A User Selecton Method n Advertsng System Shy XIOG, Zhqng LI, Bo XIAO Pattern Recognton & Intellgent System Lab,Bejng Unversty of Posts and Telecommuncatons, Bejng, Chna Emal: xongshy@gmal.com, {lnzq, xaobo}@bupt.edu.cn Receved October 27, 2009; revsed ovember 28, 2009; accepted December 30, 2009 Abstract It s mportant for moble operators to recommend new servces. Tradtonal method s sendng advertsng messages to all moble users. But most of users who are not nterested n these servces treat the messages as Spam. Ths paper presents a method to fnd potental customers who are lkely to accept the servces. Ths method searchs the maxmum frequent temsets whch ndcate potental customers features from a large data set of users nformaton, then fnd potental customers from those maxmum frequent temsets by usng a bayesan network classfer. Expermental results demonstrate ths method can select users wth hgher accuracy. Keywords: User Selecton, Maxmum Frequent Itemsets, Bayesan etwork 1. Introducton Recent years, as the ncreasng number of moble operators and new servces, the competton between operators becomes more and more furous. The key of the competton s to wn more customers. How to ntroduce new servces to proper customers becomes one of the major problems. ow, advertsng system mostly uses these technologes: collaboratve flterng technology [1], content based recommendaton, knowledge based recommendaton, effcency based recommendaton, assocaton rules based recommendaton [2], etc. These methods are usually used to select and recommend products to specfed users. They analyse the user s hstorcal data and predct what products the user mostly need. For example, an onlne move system uses one of above technologes to serve ts user whose name s Mke. The system wll analyse Mke s hstory and recommend hm some moves n whch he may be nterested. So we can say these methods solve the problem to select products for users. The proplem we desre to solve n ths paper s a lttle dfferent. Here we have only one product, namely the new moble servce, and we want to know whch users want to buy ths servce. Those users are potental customers, and we need to pck out them from a large number of users. The problem s to select users for one product. As a result, those tradtonal methods wll not work well on ths problem. Ths paper presents a new method to select users. Ths method searchs the maxmum frequent temsets from a large data set, then fnd potental customers from those maxmum frequent temsets by usng a bayesan network classfer. 2. Analyss of Customer Features Usually, moble operators keep a large database of consumpton data. Ths data ncludes the tems that represent customers features, such as the telephone fees of every month, functon fees and nformaton fees, etc. Ths data also shows whether a customer accepts the new servce, those customers who accept new servce can be seen as postve samples, and others can be seen as negatve samples. As a result, the problem to select postve samples can be treated as a two-category classfcaton problem. Usually, two-category classfcaton problem should have data wth features shown n Fgure 1(a). However, the moble users data are qute dfferent (shown n Fgure 1(b). For example, some users who have the same features may belong to dfferent categores. What s more, number of negatve samples s much larger than the number of postve sample. Because of ths, routne classfcaton methods such as nave bayesan classfer s hard to classfy such data. 3. Frequently Itemsets 3.1. Fomal Model of Frequently Itemsets Let sample X has n dmensons(attrbutes) and the value may be contnuous. Dvde the value nto several seg- Copyrght 2010 ScRes.

2 S. Y. XIOG ET AL. 55 x2 O O x2 (a) (b) x1 x1 Fgure 1. Feature dstrbuton map. (a) Two-category classfcaton; (b) Moble users data. ments and gve each segment a unque seral code. Because each sample belongs to an segment, we can use the correspondng segment code to replace ths sample. In ths way, orgnal samples X are mapped to the segment codes Y : F : X Y. Dvde the orgnal samples uses the followng gudelnes: 1) The number of postve samples n each segment s unformly dstrbuted. 2) Each Attrbutes should have the same number of segments. Make each Attrbute dvded nto k segments so that the segments code should not bgger than k. If the value of a Attrubutes s a 0-1 type value, the number of segments code should be {0,1}, not {0,1,..., k -1}. The Attrbute segment s called tem. The Attrbute segment combnaton of mm ( n) attrbutes {,,..., } s called temsets. m s length of the temsets I. If a sample s attrbute segments n m dmensons are,,...,, we call ths sample satsfed the temsets. I If an temsets has postve sample number a whle has negatve sample number b, f a/( a b) s the success rate of temsets I. Gven a mnmum support threshold. If a >, we call I frequent temsets. j 3.2. Frequent Close Itemset Gven I {,,..., } be an temsets wth length m, add an tem of attrbute m 1 nto I, so I ' {,,...,, } m 1 has the length m 1. If I and I ' have the same number of postve samples, then f f. Ths can be proofed as below: Suppose both I and I ' have a postve samples. I has b negatve samples, whle I ' has b '. Snce I ' has one a a more tem m 1,we can get b b '. Thus a b, a b' th at s f f, and I ' has a hgher success rato. In case of f f, gnore the temsets I, and keep I '. When f f, I s called frequent close temsets(ab. FCI). There are some typcal algorthm to mne frequent close temsets such as Apror [3] and FP2Tree [4]. As a part of the user selecton method, samples n the FCI should be predcted usng Bayesan network classfer. Gven the number of attrbutes n and the number of segments k, then the number of temsets s: 1 C M ( M 1) 1 The number has an exponental growth, so t s mpossble to construct a Bayesan network classfer for all temsets. A compromsed way s colletng all of the samples to be one data set and construct a Bayesan network classfer based on ths data set. 4. Bayesan etwork Model for User Selecton 4.1. Bayesan etwork Bascs A Bayesan network s a graphcal model that encodes relatonshps among varables of nterest. A Bayesan network conssts of a set of nodes and a set of drected edges between nodes [5], whch shown n Fgure 2. In general, a Bayesan network s expressed as sgn B( GP, ), whch conssts of the followng two parts [6]. Fgure 2. Typcal graphc model of Bayesan networks. Copyrght 2010 ScRes.

3 56 S. Y. XIOG ET AL. 1) A drected acyclc graph G wth n nodes. The nodes of the graph represent random varables or events. The drected edges between nodes n the graph represent the causal relatonshps of the nodes. The mportant concept n Bayesan networks s the condtonal ndependence between varables. For any varable V, the parent varables of V s pa( V ), the s ndependent of the V varables set A( V ), whch s the set of the varables that are not chld varables of pa( V ), so the probablty of s calculated as: V log MDL( B D) B LL( B D) 2 where B s the number of parameters n the network. The second term s the negaton of the log lkelhood of B gven D : LL( B D) log( P ) B( u ) 1 Bayesan network structure learnng s P problem, now typcal method has K2 [8] developed by Cooper and Hers2kovts. pv ( AV ( ), pav ( )) pv ( pav ( )) 2) A condtonal probabltes table (ab. CPT) asso- 5. Model of Selecton Method cated wth each node P. The CPT s expressed as PV ( pav ( )) whch pctures the mutual relatonshp of The method searchs the maxmum frequent temsets each node and ts parent nodes. odes wth no parent from a large data set, then fnd potental customers from have a very smple probablty table, gvng the pror those maxmum frequent temsets by usng a bayesan probablty dstrbuton of the node. odes wth parents network classfer. In detal, we can get result by takng are much more complcated. These nodes have cond- followng steps: tonal probablty tables, whch gve a probablty dstr- 1) All users share the same attrbutes. For each buton for every combnaton of states of the varable s attrbute, users have dfferent values from each other. We parents. get all sample s values of each attrbute from the data set, The Bayesan network can represent all of the nodes and dvde these values nto k segments usng the method jont probablty due to the node relatonshp and the presented n Subsecton condtonal probablty table. Applyng the condtonal 2) Based on the data processng n step 1, set a ndependence nto the chan rule, we get the followng mnmum support number and mne all of the FCI. expresson: 3) Every FCI contan a certan number of samples, collect these samples and make them to be a new data set n pv (, V,... V) pv ( pav ( )) D. 1 2 n 4) Learn bayesan network from data set D whch s Bayesan etwork Structure Learnng Consder a fnte set U { V, V,... V of dscrete random 1 2 n } varables where each varable V may take on values from a fnte set, denoted by Val( V ). Formally, a Bayesan network for U s a par B G,, and defnes a unque jont probablty dstrbuton over U. The problem of learnng a Bayesan network can be nformally stated as: Gven a tranng set D { u, u,... u } of nstances of U, fnd a network 1 2 n B that best matches D. The common approach to ths pro- blem s to ntroduce a scorng functon that evaluates each network wth respect to the tranng data, and then to search for the best network accordng to ths functon. The two man scorng functons commonly used to learn Bayesan networks are the Bayesan scorng functon [8], and the functon based on the prncple of mnmal descrpton length (ab. MDL) [7]. We only ntroduce MDL scorng functon. The MDL scorng functon of a network B gven a tranng data set D, wrtten MDL( B D ), s gven by followng expreson: formed n step 3, usng K2 algorthm. 5) For samples wthout knowng s postve or s negatve, calculate ts postve probablty by bayesan network constructed n 4. Once we get the probablty, we can get result by comparng t wth gven threshold. If a user s predcted to be postve, then the recommendaton system wll send the advertsement message to ths user. On the other hand, user who are predcted to be negatve wll not receve advertsement messages. 6. Experment In experments we try the new method as well as method based on nave bayesan claasfer so that we can compare the results between both methods Classfy Usng ave Bayesan Classfer 1) Dscretze the values and tran nave bayesan classfer. 2) Gve a decson threshold and classfy the test data set usng the traned nave bayesan classfer. Results are shown n Table 1. Copyrght 2010 ScRes.

4 S. Y. XIOG ET AL. 57 Success rate umber of segments Fgure 3. Success rate for dfferent segments number Mnmum support number counter Fgure 4. Success rate for dfferent Mnmum support number. Success rate Fgure 5. Bayesan network learned by GEIE Steps of ew Method 1) We dvde the values of each attrbute nto k segments. Whle k s 10, 20, 30 respectvly, the results are shown n Fgure 3. It shows that the best result occurs whle k s 10. 2) Gven a mnmum support number and search FCI, then compare the expermental results when s 10, 20, 30, 40, 50 respectvly. The results are shown n Fgure 4. It shows that we get the hghest success rate whle s 50. We don t consder a mnmum support number larger than 50 because the coverage of potental customer wll decrease too much, see the data n Table 2. 3) Learn bayesan network from D usng a open source bayesan tool called GEIE ( whch uses K2 algorthm to learn Bayesan network structure. We get a Bayesan network shown n Fgure 5, where GPRS_ FLOW, MMS_FEE, etc are names of attrbutes. YD- SUCCESS s the target attrbute to be predcted. 4) Last step s predctng by Bayesan network classfer. Snce the number of negatve samples s much more large than number of postve samples, we set the threshold to be 0.1. That means f a sample has a probablty to be postve sample larger than 0.1, then t s determned to be postve Expermental Results In ths experment, we use a data set from a moble servce provder. We dvde the users nto two parts. One part wth the user number of s tranng data set. The other part wth users ncludng 577 postve users s test data set. For tradtonal method whch sends advertsng message to all users. It sends advertsements and gets 577 customers, the success rate s 4.7%. It sends advertsement to 100% of the users, and gets 100% of the potental customers. For method based on naïve Bayesan classfer whose results shown n Table 1, the results are almost the same as the results of tradtonal method. Obvously, naïve Bayesan classfer does not work well wth moble user s data set. For new method, the results are shown n Table 2. Take mnnum support number 50 for example, t sends 1621 advertsements and gets 303 customers, the success rate s 18.69%. It sends advertsement to 13.22% of all users, and gets 49.78% of the potental customers. The results show that ths user selecton method ncreases success rate effcently. Even the lowest success Copyrght 2010 ScRes.

5 58 S. Y. XIOG ET AL. Mn support umber Table 1. Results of naïve Bayesan classfer. Threshold Postve Correct Success rate % % % / Ta ble 2 Experment result. Pos tve Correct Success rate Cost Coverage of potental customer % 46.55% 92.73% Recomendaton system becomes more and more mpor- tan t to servce provders now. User selecton s one of the dffculty problems. Ths paper presents a method to select users, the method searchs the maxmum frequent temsets from a large data set, then fnd potental customers from those maxmum frequent temsets by usng a bayesan network classfer. The success rate s mproved dramatcally after usng the method. Ths method also cuts down advertsement cost for moble operators and avods makng large number of Spam messages. There are a lot of data sets whch have smlar features wth moble user s data set, so ths method can be used n many smlar advertsng recommendaton systems. It has a good unversalty. 8. Acknowledgement Th s work was supported by the natonal Hgh-tech Research and Development Plan of Chna under grant o.2007aa01z417 and the 111 Project of Chna under grant o. B References % 43.55% 84.95% [1] E. Rch, User modelng va stereotypes [D], Cogntve % 35.15% 71.71% Scence, Vol. 3, o. 4, pp , % 27.30% 63.86% [2] U. M. Fayyad, G. Patecsky-Shapro, and P. Smyth, % 13.22% 49.78% Advances n knowledge dscovery and data mnng [M], Calforna, AAAI/MIT Press, rate 9.85%, s dramat cally hgher than the success ra te [3] R. Agrawal and M. Srkant, Fast algorthms for mnng of tradtonal method and of naïve Bayesan classfer. It assocaton rules n large databases [R], IBM Almaden also saves advertsng cost and cuts down Spam remarkably. Research Center, Tech Rep: RJ9839, [4] J. W. Han, J. Pe, Y. W. Yn, et al., Mnng frequent patterns wthout canddate generaton: A frequent-pattern 7. Conclusons tree approach [J], Data Mnng and Knowledge Dscovery, Vol. 8, o. 1, pp , [5] F. V. Jensen, An ntroducton to Bayesan networks, Sprnger, ew York, [6] M. desjarns, Representng and reasonng wth probabl- Bayesan stc knowledge: A Bayesan approach, Uncertanty n Artfcal Intellgence, pp [7]. Fredman, D. Geger, and M. Goldszmdt, network classfers, Machne Learnng, pp , [8] G. Cooper and E. Herskovts, A Bayesan method for the nducton of probablstc networks from data [D], Machne Learnng, Vol. 9, o. 4, pp , Copyrght 2010 ScRes.