Volume 18 No.13, October 015 An Approach for Recommender System by Combnng Collaboratve Flterng wth User Demographcs and Items Genres Saurabh Kumar Twar Department of Informaton Technology Samrat Ashok Technologcal Insttute Vdsha, M.P., Inda Shalendra Kumar Shrvastava, PhD Department of Informaton Technology Samrat Ashok Technologcal Insttute Vdsha, M.P., Inda ABSTRACT Wth the exploson of servce based web applcaton lke onlne news, shoppng, bddng, lbrares great amount of nformaton s avalable. Due to ths nformaton overload problem, to fnd rght thng s a tedous task for the user. A recommender system can be used to suggest customzed nformaton accordng to user preferences Collaboratve flterng technques play a vtal role n desgnng the recommendaton systems. The collaboratve flterng technque based recommender system may suffer wth cold start problem.e. new user problem and new tem problem and scalablty ssues. Tradtonal K-Nearest Neghbor Technque also suffers wth user and tem cold start problem.in ths paper recommender system generates suggestons for user by combnng collaboratng flterng on transacton data wth ratng predcted wth user demographcs and tem smlarty. The fnal ratng s weghted sum of ratngs computed from transacton data, user data and tem data. The advantage of proposed system that recommender system can deal wth cold start n case of "new user" or new tem.and Also system has low MAE and RMSE n comparson of tradtonal collaboratve flterng based on K-Nearest Neghbor approach. Keywords Recommendaton System, Collaboratve Flterng, Cold start, demographc flterng, K-Nearest Neghbor Method. 1. INTRODUCTION Wth the dramatcally fast and explosve growth of knowledge on the market over the Internet, World Wde Web has become a robust platform to store, spread and retreve data lkewse as mne helpful data. As a result of the propertes of the large, dverse, dynamc and unstructured nature of web data, web data analyss has encountered lots of challenges, lke scalablty, multmeda system and temporal problems etc. Due to ths large amount of nformaton fndng nterestng nformaton s a tedous and tme spendng task for the user. The scope of Recommendaton system here comes nto lght. Recommendaton Systems are software tools and technques that deal wth nformaton overload by provdng nterestng suggestons and recommendatons to users [9]. These recommendatons help users to make decson as whch tem to buy or whch musc to lsten or whch onlne news to read. Recommendaton Systems are prmarly focused on type of tems lke book recommendaton system or musc recommendaton system etc. In ts commonest formulaton, the recommendaton problem s reduced to the problem of estmatng ratngs for the tems that haven't been seen by a user. Intutvely, ths estmaton s typcally based on the ratngs gven by ths user to other tems. The recommendatons provded by RS may be personalzed or non-personalzed. The frst, personalzed RS may provde dfferent recommendaton for a dverse set of users accordng to ther nterests respectvely. The other varant, non-personalzed RS wll gve a smlar set of recommendatons to dfferent users lke Top 10 books or Top 10 songs etc. Second, non-personalzed RS are smple and recommendatons are easy to generate. They may observe n onlne magaznes or n onlne newsreaders. These styles of non-personalzed recommendatons don't seem to be usually addressed by RS analyss [9]. Recommender systems work as nformaton processng systems whch gather mostly of three categores user data, tems data and transacton nvolvng users and tems wth preferences. User may have dfferent characterstcs and preferences. In order to generate personalzed recommendatons RSs uses a dverse range of user nformaton. User nformaton can be modeled n many ways and the selecton depends on technque of recommendaton. User can also be narrated wth ther onlne behavor or navgatonal patterns. Item refers to the object whch wll be recommended to user. News, musc, books, moves etc all are tems n context of recommendaton system. Item can be descrbed wth attrbutes assocated wth t. For example a move can be descrbed wth move name, drector, genre, cast etc. Transactons are the recorded nteracton between user and tem. Transacton mostly s tabular data that record mportant nformaton durng human computer nteracton. Transacton may contan user feedback explctly. Also user tastes can be understood by lookng nto the transactons. User preferences are measured n terms of onlne navgatonal patterns or ratngs provded by the user. Data for recommendaton system may be mplct or explct. Implct data are recorded from user clck streams, hyperlnk navgaton whle explct data s found n form of ratngs or feedback provded by user for an tem. Recommendaton systems embrace processes that are conducted for the most part by hand, lke manually makng cross-sell lsts and actons that are performed for the most part by PC, lke collaboratve flterng. The latter referred as automatc recommendaton systems. Automatc recommendaton systems are specalzed data processng systems that are optmzed for nteracton wth customers nstead of marketers. They need been explctly desgned to requre advantage of the real-tme personalzaton opportuntes web based servces, accordngly, the algorthms focus addtonal on real-tme and just-n-tme learnng than on model-buldng and executon [6]. In Collaboratve flterng based RS the user wll be suggested 16
Volume 18 No.13, October 015 tems that people wth smlar nterested and preferences lked n the past. In a CF recommendaton applcaton, n order to suggest tems to user, the collaboratve flterng recommendaton system looks for the peers of user,.e., set of users that have smlar nterest n tem. Then, only the tems that are most lked by the peers of user would be suggested [1]. In demographc flterng RS, t s assumed that the users wth common demographcs wll also have same tastes and preferences []. Many webstes adopt smple and effectve personalzaton solutons supported demographcs. For example, users are dspatched to specfc webstes supported ther language or country. Or suggestons may be customzed accordng to the professon or age of the user [9].The other type s Hybrd flterng, n whch RS generates recommendatons combnng features of dfferent flterng technques. Most common combnatons are Collaboratve flterng wth Content based or Collaboratve flterng wth demographc flterng [1, ]. Wdely accepted taxonomy classfes recommendaton methods nto Memory based approach and Model based approach. Memory-based methods usually use smlarty metrcs to obtan the dstance between two users, or two tems, based on each of ther attrbutes. Model based use RS nformaton to create a model that generates the recommendatons [1]. Memory-based algorthms use the full table to calculate ther predcton. They use smlarty measures to choose users or tems that are the same as the actve user. Now, the predcton s calculated from the ratngs of those smlar users or tems. Most of those algorthms wll be classfed as user-based algorthms or tem-based algorthms dependng on whether the process of gettng neghbors s concentrated on fndng smlar users [5]. In Model based approach,the desgn and development of models usng machne learnng, data mnng algorthms can enable the system to learn to recognze complcated patterns based on the learnng nformaton, then generate ntellgent predctons for test data or real-world nformaton, based on the learned models[4]. Model based algorthms use the collectons of ratngs to learn a model and ths model s employed for generatng ratng predctons [1]. The organzaton of the paper s as follows: In Secton, related work s brefly dscussed. In 3 rd secton, the proposed system s elaborated, whch combnes tem-based collaboratve flterng wth user clusters based on demographcs and genre based tem smlarty n a hybrd approach. In Secton 4, the performance of the proposed system s dscussed to show how t acheves a reduced MAE and successfully solvng the cold start problem. In 5 th secton, the concluson of paper s presented. RELATED WORK In collaboratve flterng based recommendaton system, system generates ratngs for the actve user based on the ratng gven by the recommender system users who are much smlar to actve user. If two users rate an tem smlarly, users are consdered smlar n the recommendaton system []. The easest and orgnal mplementaton of ths approach [3] suggests to the actve user the tems that dfferent users wth same preferences wthn the past. The smlarty n style of two users s calculated supported the smlarty wthn the ratng hstory of the users. ths can be the ratonale why [6] refers to collaboratve flterng as collaboraton among users of recommender system. collaboratve flterng s taken nto account to be the foremost common and wde enforced technque n RS. In Table 1, frst we have to estmate the potental favorable opnon of Steve about Harry potter, one can use the smlarty of her wth those of John. Table 1: Recommendaton Process n Nutshell Move Person John Boby Steve Ttanc 5 1 5 The Reader Harry Potter 1 5 4? Alternatvely, one can note that ratngs of Ttanc and Harry potter follow a same pattern, whch shows that people who lked the former mght also lke the later [17]. An example gven n Table 1 wll gve bref knowledge about collaboratve flterng. Collaboratve flterng technques mostly reled upon K nearest neghbors methods to predct recommendatons for the user. In Artcle recommender by GroupLens t was frst ntroduced. There are two verson used of k Nearest Neghbor approach n collaboratve flterng.1 User-based collaboratve flterng In ths collaboratve flterng approach, recommendaton tems are predcted on the bass of fndng recommendaton system users wth same tem preferences to the actve user.the methodology can be llustrated n followng three steps [16]: 1. By usng a partcular smlarty measure recommendaton system produce a set of smlar users to the actve user u. The selected K users are the K closest (smlar neghbor to actve user u.. Once k close neghbors are found to actve user u, predctons are generated for tem by usng any one of followng aggregaton approach, the average, weghted sum, and the weghted adjusted aggregaton. 3. To have top n recommendaton, n tems wll be chosen from the smlar tems that are close neghbor of actve user. User based collaboratve flterng suffers wth scalablty problem.. Item-based collaboratve flterng As the number of users ncreases User to user based knn suffers from scalablty problem. To overcome ths drawback new method called tem to tem K-NN s ntroduced by Sarwar et al. [17] and Karyps. The tembased approach nvestgates the set of tems rated by target user and calculates ther smlarty wth the target tem and then chooses k most smlar tems1,, k. Ther representng smlartes t 1, t t k are also computed at the same tme. Formerly the most smlar tems are dscovered, after that by takng a weghted mean of the target user's ratngs on these smlar tems the predcton s calculated. Smlarty computaton and 17
Volume 18 No.13, October 015 the predcton generaton are two mportant factors whch make tem-based recommendaton more powerful. For smlarty computaton bascally dfferent types of smlarty measures are used and weghted sum and regresson used for predcton computaton. Collaboratng flterng based recommendaton system also faces some ssues [1, ]. a New User Problem It s dentcal drawback lke content-based systems. n order to generate correct recommendatons, the system should ntally learn the user s preferences from the ratngs that the user offers. Many technques are projected to deal wth ths drawback. Most of them use the hybrd recommendaton approach, whch mxes content-based and collaboratve technques. b New Item Problem New tems are added frequently to recommender systems. Collaboratve systems trust only on user s preferences to generate recommendatons. Therefore, tll the new tem s rated by a consderable range of users, the recommender system would not be able to recommend t. Ths downsde may also be addressed explotaton hybrd recommendaton approaches, represented n the next secton. c Sparsty In any recommender system, the amount of ratngs already obtaned s typcally very lttle compared to the amount of ratngs that requre to be expected. Effectve predcton of ratngs from a lttle range of examples s very mportant. Also, the success of the collaboratve recommender system depends on the provson of an mportant mass of users. for nstance, n the move recommendaton system, there could also be several moves that are rated by solely few ndvduals and these moves would be recommended terrbly seldom, although those few users gave hgh ratngs to them. Also, for the user whose tastes are uncommon compared to the remander of the populaton, there wll not be the other users who are sgnfcantly smlar, resultng n poor recommendatons [9]..3 Smlarty Measure: Memory-based CF algorthms check for the complete or a sample of the user-tem data to create a predcton. Every user s a part of a group of people wth smlar nterests. By dentfyng the supposed neghbors of a current user (or actve user a predcton of tastes on new tems for hm or her are gong to be generated. The neghborhood-based collaboratve Flterng rule, a current memory-based CF rule, uses the followng steps: 1. calculate the smlarty or weght: calculate the smlarty or weght, w j, that reflects dstance, correlaton, or weght, between two users or tems, and j;. Generate a predcton for the actve user by takng the weghted average of all the ratngs of the user or tem on a defnte tem or user, or employng an easy weghted average [17]. When the task s to buld a top-n recommendaton, we want to search out k most smlar users or tems (nearest neghbors once computng the smlartes, so aggregate the neghbors to urge the top-n most frequent tems as the recommendaton. Smlarty computaton between tems or users could be a essental step n memory-based collaboratve flterng algorthms. For tem-based CF algorthms, the essental plan of the smlarty computaton between tem and tem j s ntal to fgure on the users who have rated each of those tems so to apply a smlarty computaton to work out the smlarty, w j, between the two co-rated tems of the users [4]. For a user-based CF algorthmc rule, we tend to ntal calculate the smlarty, w uv, between the users u and v who have each rated a smlar tems. There are many varous ways to work out smlarty or weght between users or tems..3.1 Correlaton-Based Smlarty In ths case, smlarty w uv between two users u and v, or wj between two tems and j, s computed by computng the Pearson correlaton or dfferent correlaton-based smlartes. Pearson correlaton measures the extent to that two varables lnearly relate wth one another [4]. For the user based algorthmc rule, the Pearson correlaton between user u and v s ( r r ( r r u u v v I wuv ( ( ru ru ( rv rv I I Where I summatons are over the tems that both the users u and v have rated and r u s the average ratng of the corated tems of the u th user. For the tem-based algorthm, denote the set of users u U who rated both tems and j, then the Pearson Correlaton wll be wj ( ru r uu ( ru r ( ruj rj uu ( ruj rj uu Where r u s the ratng of user u on tem, r s the average ratng of the th tem by those users..3. Vector Cosne-Based Smlarty The smlartes between two documents are often measured by treatng every document as a vector of word frequences and computng the cosne of the angle formed by the frequency vectors [39]. Ths formalsm may be adopted n collaboratve flterng, that uses users or tems rather than documents and ratngs rather than word frequences. Formally, f R s that the m n user-tem matrx, then the smlarty between tems, and j, s outlned as the cos of the n-dmensonal vectors chersh the th and jth column of matrx R. Vector cosne smlarty between tems and j s gven by j w j cos(, j (4 * j Where denotes the dot-product of the two vectors. To get the desred smlarty computaton, for n tems, an n n smlarty matrx s computed [4]. For example, f the vector A ={x 1, y 1 }, vector B ={x, y }, the vector cosne smlarty between A and B s w j j cos(, j * j (5 ( x 1 x x y y 1 y 1 1 ( x (3 y.3.3 Adjusted Cosne Smlarty Adjusted cosne smlarty s also a smlarty measure whch 18
Volume 18 No.13, October 015 s used n collaboratve flterng based recommender system. It s used n the case n whch dfference n every user's use of ratng scale s consdered [8] ( r r ( r r u, u u, j u uu ' s (, j (6 ( ru, ru ( ru, j ru uu ' uu ' Where U s referred to the set of users who had gven ratngs to both tem and j and the average ratng of user u s r u. In an approach, tem based method s used to allevate sparsty and user clusters are formed to acheve hgh scalablty. It also combnes tem based and user based collaboratve by provdng a weghted average of predctons [38]. However, these algorthms do not gve soluton for the cold start problem. To solve the problems of scalablty and sparsty n the collaboratve flterng, an approach s gven n [39] n whch personalzed recommendaton methods jons the user cluster and tem cluster. Ratngs gven by users on tems are used for user cluster, and each users cluster s gven by a cluster centre. User s neghbor s calculable by computng smlarty between actve user and cluster centres. Then, the gven approach employs the tem based collaboratve flterng based on clusterng to generate the recommendatons. Ths offers scalable and correct recommendaton then tradtonal approach by provdng recommendaton combnng user cluster and tem cluster based collaboratve flterng. In recent tmes numerous enhancements to tradtonal approach of collaboratve flterng are proposed ncludng change n user s preferences wth reference to tme [40] tacklng the sparsty and scalablty problem, trust on users, and evoluton of hybrd recommender system. To mprove the predcton qualty of tem-based collaboratve flterng, some algorthms take the attrbutes of tems nto consderaton whle predctng the preference of a user [41]. There s an attempt to cope wth Item cold start usng a hybrd method whch frst clusters tems usng the ratng matrx and then uses the clusterng results to buld a decson tree to combne novel tems wth exstng ones [4]. Collaboratve flterng, content based flterng and demographc flterng have been combned to solve the cold start problem [43]. However, they do not address the scalablty problems of user based collaboratve flterng. Clusterng of users has been used to solve the scalablty problem of user based algorthms. In one approach, a cascaded hybrd model frst clusters users based on demographc data and then apples user based collaboratve flterng to each cluster [44]. In [45] a metrc s gven to estmate smlarty between users s gven, whch can be appled n collaboratve flterng technque n recommender systems. The metrc s formulated by the use of a lnear combnaton of values and weghts. Values are computed for every par of users for whch the smlarty s calculated, at the same tme as weghts are computed just once,usng a precedng step n whch a genetc algorthm extracts weghs from the recommender system whch depends on the precse nature of the data from every recommender system. Ths results n sgnfcant mprovements n qualty of predcton and recommendaton and performance. In [46], a hybrd algorthm s proposed by combnng the ratngs and content data to overcome tem cold-start problem. In ths approach ntally tems are clustered based on the ratng matrx and clusterng results and tem content data are utlzed to create a decson tree to assocate the promnent tems wth the exstng tems. Consderng the constantly ncreasng ratngs on novel tem, there s a tendency to present predctons of ths method can be assocated wth the tradtonal collaboratve-flterng strateges to meet wth hgher performance wth a coeffcent. Tests performed on data set show the development of recommender approach n handlng the tem sde cold-start problem. In many real recommender systems, great porton of tems are new tems and recommendng new tems to consumers s a key success for onlne enterprsers. A hybrd approach s developed whch explots not only ratngs space but also attrbutes of tems for tem cold-start recommendaton. In [47] an enhanced collaboratve flterng recommendaton algorthm s proposed based on dynamc tem clusterng method. Item space s dvded nto clusters dynamcally by ntroducng a smltude threshold model. They stated wth experments that by employng dynamc tem clusterng method recommender system can convnce the requrement of ncreasng amount of users and consumers n huge e- busness systems. The stated collaboratve Flterng recommendaton algorthm works comparatvely good n provdng recommendaton wth mnmze resource consumpton. Hybrd approaches have also been proposed to mprove the accuracy of predctons. Adaptve weghted predcton has been used to calculate fnal ratngs from user-based and tembased approaches [48].The method n [30] uses Pareto domnance to carry out a pre-flterng process to elmnate less delegate users from the k-nearest neghbor selecton procedure whle retan the most promsng ones. The computatons from the MoveLens and Netflx webstes show vtal mprovement n qualty measures. In [3] K-Nearest-Neghbor (KNN classfcaton s employed to be used on-lne to spot clents/vstors clck stream knowledge, matchng t to a specfc user group and advocate a talored browsng choce that meet the necessty of the precse user at a selected tme. They stated that the K-NN classfer s clear, consstent, smple, easy to know, hgh affnty to have desrable qualtes and straghtforward to mplement than most alternatve machne learnng algorthms specfcally once there s very lttle or no prevous nformaton regardng data dstrbuton.in [8], tem ratngs from tem based collaboratve flterng recommender technques are assocated wth ratngs computed from user clusters based on demographcs n a weghted manner. The stated soluton s scalable and successfully overcome user based cold start. At performance front proposed recommender system generate recommendaton wth comparatvely reduced MAE and better coverage to nearest neghbor based collaboratve flterng recommenders. However, tem clod start s not addressed. 3. PROPOSED SYSTEM Collaboratve flterng based recommender system suffers from scalablty, sparsty and cold start stuatons lke new user occurs or new tem occurs. These problems have been dscussed above. 19
Volume 18 No.13, October 015 The proposed system s shown n fgure 1.In recommender system there are three sources of data exst- Transacton data- contans ratngs for tems provded by users. User Demographcs-lke age, gender, occupaton or locaton Item genres-tem may belong to one or more genres e.g. a move may belong to acton, comedy genres. In proposed system all three types of data are used. For a user-tem par ratngs are estmated from all three types of data and fnal ratng s computed as weghted sum of three ratngs. Workng of system s dscussed n followng sectons- 3.1 Ratng Predcton from Transacton Data Ratng from transacton data are predcted usng K-Nearest neghbor classfcaton technque. K-nearest neghbor [11] s mostly used algorthm for collaboratve flterng based technques. Here K denotes the number of neghbors. Its prmary vrtues are smplcty and reasonably accurate results. In the tem to tem verson [] of the knn algorthm, the followng two tasks are executed: 1. Determne k tems neghbors for each tem n the database;. for each tem not rated by the actve user a, calculate ts predcton based on the ratngs of a from the k neghbors of K-nearest neghbor algorthm now provdes ratng for actve user a based on transacton data. r t shows the ratng estmated from K-nearest neghbor classfcaton performed on transacton data 3. Ratng Predcton from User Demographcs Users are parttoned n dfferent cluster usng K-means clusterng algorthm [11] by usng user s demographcs. Ratngs are computed n followng steps: 1. Frst smlarty of actve user s demographcs s calculated from all clusters. Here Pearson correlaton s used as smlarty measure. Maxmum smlarty value decdes the cluster for actve user.. Ratng for actve user s gven by multplyng smlarty measure wth average ratng of cluster n whch actve user les. r u s ratng estmated by user clusterng based on user demographcs. K-NN Classfcaton on ratng data Predcted Ratng r t Ratng data User demographcs Item features User Clusters Based on Demographcs Ratng estmaton by computng smlarty wth user clusters r u Combng Ratngs usng a weghted scheme r * r t * r u * r Top N recommendaton for User Fgure 1: Proposed System Archtecture. 3.3 Ratng Predcton from tem genres Also for tem K-means parttoned based clusterng algorthm s performed. Items are clustered by usng ther genres (for move, musc, books. Other features of tems can also be used for tem clusterng. Ratngs are computed n followng steps: Item Clusters Based on tem Features Ratng estmaton by computng smlarty wth tem clusters r 1. Frst smlarty of the tem, on whch ratng s to be predcted, s calculated from all clusters. Here agan Pearson correlaton s used as smlarty measure. Maxmum smlarty value decdes the cluster for current tem.. Ratng for tem s gven by multplyng smlarty measure wth average ratng of tems n the cluster to whch current tem s most smlar. r s ratng estmated by tem clusterng based on tem genres. 0
Volume 18 No.13, October 015 3.4 Combnng Ratngs The resultant ratng s estmated as weghted sum of three ratngs r t, r u and r n followng manner r * r t * r u * r (7 Here r s predcted ratng. r t s ratng predcted usng classfcaton of transactons. r u s calculated ratng based on user smlarty. r s calculated ratng based on tem smlarty.,, are weghts for dfferent calculated ratngs determned by experment. Values of,, are emprcally decded n such a manner that 1 (8 Once combned ratng s calculated, Top N tems not yet seen are recommended to actve user. K-Nearest Neghbor classfcaton, user clusterng and tem clusterng are performed offlne and recomputed at a certan perod of tme. Hence t ensures scalablty of system. Now f there s a user who has not rated any tem n the past the transacton ratng s nothng. So the ratng wll be predcted on the bass of user demographc and the tem genres. Ths solves user-sde cold start problem. Smlarly f there s a new tem whch s not rated yet, agan ratng n transacton wll be zero. So ratng for that tem to the actve user wll be computed by user demographcs and tem genres. Ths solves tem-sde cold start problem. The system s evaluated usng mean absolute error (MAE and root mean square error (RMSE, gven as followng 1 MAE n u, RMSE p u, ru. 1 ( p u, ru, n u, (9 (10 4. RESULTS Data from the MoveLens [10] data set was used to test the system. MoveLens data sets were collected by the GroupLens Research Project at the Unversty of Mnnesota and are a popular choce for research on recommendaton systems. It conssts of 100,000 ratngs from 943 users on 168 moves. The ratngs are n the scale of 1-5 where 1 means Awful and 5 means Must see. In the dataset, each user has rated at least 0 moves. Smple demographc nfo for the users such as age, gender, occupaton and zp code s ncluded. The ratngs dataset U was dvded nto two tranng sets (UABASE and UBBASE and correspondng test sets wth exactly 10 ratngs per user beng wthheld n the test set. The test sets UATEST and UBTEST were dsjont. The ratng predcton was taken as the average of experment results over the two datasets UA and UB. Then, the MAE and coverage of UBCF, IBCF, DBCF and IDBCF were compared. 4.1 Expermental Setup The proposed system s tested wth MoveLens dataset [14]. Frst experments were conducted to determne the number of neghbors for K-nearest neghbor classfcaton. The values of k are tested n step of K=1, 3, 5 9, total 15 sets of values were tested. Also values of weghts,, were tested wth 11 sets of dfferent,, values. Sets used n experment s gven n table Table : Dfferent sets of Weghts Set 1.04 0.3 0.3 0.4 0.4 0. 3 0.4 0. 0.4 4 0.5 0.3 0. 5 0.5 0. 0.3 6 0.6 0.1 0.3 7 0.6 0. 0. 8 0.6 0.3 0.1 9 0.7 0. 0.1 10 0.7 0.1 0. 11 0.8 0.1 0.1 The lowest MAE was obtaned when =0.6, =0.1, =0.3 and K=5.Snce dataset have ratng scale 1-5 so n K- means algorthm number of cluster s chosen s 5.So user and tem data s dvded n 5 clusters respectvely. The lowest mean absolute error and root mean square error s computed when number of neghbors s k=9 for classfcaton algorthm. Fgure and 3 shows the senstvty of MAE and RMSE wth changng values of set descrbed for,, and respectvely. The system generates recommendaton wth lower MAE compare to tradtonal K-nearest neghbor based collaboratve flterng technques. Also t resolves the user based and tem based cold start ssues wth a sngle mechansm. Also fgure 5 shows the mprovement of proposed system over tradtonal k nearest neghbor based collaboratve flterng. So the proposed system has almost equal MAE to IBCF and IDBCF but t solves cold start ssues n recommender system, However IDBCF handles user cold start, proposed system s able to tackle tem and user cold start wth reduced MAE 1
Volume 18 No.13, October 015 Fgure : MAE vs Set of weghts Fgure 5: MB(proposed MAE vs MK( Tradtonal MAE Fgure 3: RMSE vs Set of weghts 4. Comparson wth Other Technques Here a comparson s gven wth some promnent collaboratve flterng technques based on MAE s. UBCF (user based collaboratve flterng and IBCF (Item based collaboratve flterng were executed usng cosne smlarty gven n (4 and (5. IBCF s more accurate than UBCF.DBCF (Demographc based collaboratve flterng has hgher MAE compare to IDBCF (Item and demographc based collaboratve flterng. Table 3 gves comparson MAE of varous collaboratve flterng algorthms Fgure 4 shows the comparson of MAE among the above dscussed technques wth proposed system.. Table 3: Comparson of algorthms Algorthms Mean absolute error UBCF 0.8485 IBCF 0.7865 DBCF 0.8373 IDBCF 0.7737 Default KNN 0.904 Proposed 0.7963 Fgure 6: RMSEB (Proposed vs RMSEK (Tradtonal 5. CONCLUSION & FUTURE WORK In ths paper, a hybrd recommendaton approach s proposed by combnng nearest neghbor ratngs predcton wth ratng computed from user demographcs and tem genres. In proposed system classfcaton and user and tem smlarty computaton s performed offlne and recomputed after certan amount of tme. New user cold start s resolved by generatng mmedate ratngs based on user demographcs whle tem cold start s addressed by usng tem cluster. The system also acheves lower MAE than tradtonal K nearest neghbor algorthm used for collaboratve flterng based recommendatons. In ths work recommendaton s generated usng correlaton based smlarty measure. In future other newly developed smlarty measure can be used whch may provde better performance. 6. REFERENCES [1] Adomavcus, G., & Tuzhln, A. (005. Toward the next generaton of recommender systems: A survey of the state-of-the-art and possble extensons. Knowledge and Data Engneerng, IEEE Transactons on, 17(6, 734-749. Fgure 4: Comparson of algorthms [] Bobadlla, J., Ortega, F., Hernando, A., & Gutérrez, A. (013. Recommender systems survey. Knowledge- Based Systems, 46, 109-13. [3] Adeny, D. A., We, Z., & Yongquan, Y. (014.
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