ISSN 1000-985, CODEN RUXUE E-mail: os@iscasaccn Journal of Sofware, Vol18, No7, July 007, 1685 1694 h://wwwosorgcn DOI: 101360/os181685 Tel/Fax: +86-10-656563 007 by Journal of Sofware All righs reserved 1+, 1 (, 510006 (, 51075 A Collaboraive Filering Recommendaion Algorihm Based on Influence Ses CHEN Jian 1+, YIN Jian 1 (School of Comuer Science and Engineering, Souh China Universiy of Technology, Guangzhou 510006, China (Dearmen of Comuer Science, Sun Ya-Se Universiy, Guangzhou 51075, China + Corresonding auhor: Phn: +86-0-33509119, Fax: +86-0-3938018, E-mail: ellachen@scueducn, h://wwwscueducn Chen J, Yin J A collaboraive filering recommendaion algorihm based on influence ses Journal of Sofware, 007,18(7:1685 1694 h://wwwosorgcn/1000-985/18/1685hm Absrac: The radiional user-based collaboraive filering (CF algorihms ofen suffer from wo imoran roblems: Scalabiliy and sarsiy because of is memory-based neares neighbor uery algorihm Iem-Based CF algorihms have been designed o deal wih he scalabiliy roblems associaed wih user-based CF aroaches wihou sacrificing recommendaion or redicion accuracy However, iem-based CF algorihms sill suffer from he daa sarsiy roblems This aer resens a CF recommendaion algorihm, named CFBIS (collaboraive filering based on influence ses, which is based on he conce of influence se and is a ho oic in informaion rerieval sysem Moreover, i defines a new redicion comuaion mehod for his new recommendaion mechanism Exerimenal resuls show ha he algorihm can achieve beer redicion accuracy han radiional iem-based CF algorihms Furhermore, he algorihm can alleviae he daase sarsiy roblem Key words: : E-commerce; recommendaion sysem; collaboraive filering; influence se,,, (, CFBIS(collaboraive filering based on influence ses, Suored by he Naional Naural Science Foundaion of China under Gran Nos60573097, 6067306 ( ; he Research Foundaion of Naional Science and Technology Plan Proec of China under Gran No004BA71A0 ( ; he Research Foundaion of Discilines Leading o Docorae Degree of Chinese Universiies under Gran No0050558017 ( ; he Naural Science Foundaion of Guangdong Province of China under Gran Nos050030, 0430046 ( ; he Research Foundaion of Science and Technology Plan Proec in Guangdong Province of China under Gran No005B1010103 ( ; he Naural Science Foundaion of Souh China Universiy of Technology under Gran NoB07E506050 ( Received 006-03-0; Acceed 006-07-05
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: 1687,,, [16], CFBIS(collaboraive filering based on influence ses,, CFBIS,,,,, m U={u 1,u,,u m } n I={i 1,i,,i n } m n A(m,n, 1 Table 1 1 A(m n user-iem raings marix - A(m,n i 1 i i n u 1 1,1 1, / u,1 /,n u m / m, m,n, m m,n n u I I,, u, u 1 i ={ 1,,,,, m, } i ={ 1,,,,, m, }, i i i,i [17] : 1 sim i,, = 1 (, i = cos( i, i =, m m (, (, = 1 = 1,, u i,, u i m, A(m,n i i, i i, U ( u U, (, i, i =, ( ( u U, u U,,, u, 3 Pearson, U u U u U, ( u U, (, i, i =, ( (,,,, i i,,, u I u
1688 Journal of Sofware Vol18, No7, July 007 u a, : (1 u a i I, ; u a ( N, N : I r I,I r = I ua, i : ( = 1 = = 1 i, i i, i, u a i, CF 3 CFBIS (,,,,,,,, (, 31 -,,-, S -, NN RNN(reverse neares neighbor [16] d S, S =n,,d(,,,,d(, Euclidean,,D(,, RNN(={ S NN(},NN RNN, NN( / RNN(, 1 r P a, i (1 S u Fig1 The relaionshis beween NN and RNN 1 NN RNN,NN(={s,},RNN(={r,,s,u}RNN 0 (
: 1689 3, CF,, CFBIS, :,, i a i b -, i a i b (relaed; i a i b -, i b i a - ( i 1 i, (,i a i b i b i a, i i 1 i i 3 i 4 Fig A simle samle of similariy relaionshis among iems CFBIS A(m,n,,, i NN(i ={i 1 }, RNN(i ={i 1,i 3,i 4 }, u a ( i, (1 i,i 0,, 3 3 Table Iem similariy able Table 3 If u a has no raings for all neares neighbor of i, hen radiional iem-based CF aroaches canno roduce redicion raings for i =1 = =3 i 1 i i 3 i 4 i i 1 i 3 i 4 i 3 i i 1 i 4 i 4 i i 1 i 3 3 u a i i 1, CF i i 1 i i 3 i 4 u? 4 5 i 1 X?? i 3 i i 4 Fig3 i lacs necessary raings for is neighbors 3 i, i, i 3 i 4 i,
1690 Journal of Sofware Vol18, No7, July 007 33, u a i, CFBIS, i NN(i R NN(i u a i 4 : P a, i ( i NN ( i i, i + i NN ( i i NN ( i = (1 i, i + i, i ( i NN ( i i, u a i,i,i i i, NN(i R NN(i i NN ( i i NN ( i (( i, i i NN ( i i NN ( i i NN ( i = + + ( i, i + i, i i, i + i, i,, i,i i i NN ( i ( i NN ( i i, i i NN ( i (( i NN ( i, i i NN ( i I ua i i NN ( i i = α + (1 α (3 i, i + i, i i, i + i, i (, i, i i NN ( i,α, α=1,, ; α=0,,α [0,1] i NN ( i i NN ( i (( i, i i NN ( i i NN ( i = α + (1 α (4 i, i + i, i i, i + i, i 34 1 Find_SimLis : - A(m,n; i NN ( i : - T NN - T RNN (( (1 A(m,n, ; i i NN ( i ( i I, i, - T NN ; (3 T NN, i I, - T RNN Find_SimLis, (1 ( CF, (3, -, ( CF O(n ( [15] 33, Find_SimLis O(n,,,, - -, CFBIS : u a, i,,,α; :u a i P (1 - T NN i, {i 1,i,,i };, i
: 1691 ( - T RNN i, {, i,, i } ; i 1 (3 (1~(4, u a i P CFBIS, (1 (3 CF, (, R NN, (1 CF O(1(, O(1 4 PC(Penium 4,CPU 4GHz, 51M, indows XP, Java 41 MovieLens Minnesoa GrouLens, eb 43 000, 3 500, 943 1 68 100 000,, 0 1~5,5 erfec, 1 bad,, x,,x=08 80%, 0%, x=08,,, ψ, 100000 ψ = 1 = 093695 943 168, 4 MAE(mean absolue error MAE MAE, [17] MAE, N { 1,,, N }, {r 1,r,,r N MAE 43 MAE = N i = 1 i ri N, 10, 50,100,150,00,50 300, 4 4,, MAE,, CFBIS
169 Journal of Sofware Vol18, No7, July 007 14 1 10 Comarison of similariy measure mehods Cosin Adused cosin Correlaion MAE 08 06 04 0 00 10 50 100 150 00 50 300 Number of neighbors Fig4 Comarison of similariy measure mehods 44 4 5, 5, 40, ;,, (3 α, α=1,, ( [15] ; α=0, α [0,1], 01 1, (,, MAE =70;, MAE =300 =, α,, 6, (, MAE (α=1 MAE, 6, (α=05,mae MAE 093 089 085 081 Comarison of he effec of differen iemses Cosin (NN_based Cosin (RNN_based Adused cosin (NN_based Adused cosin (RNN_based MAE 077 076 075 074 Comarisons of MAE on differen alha Cosin Adused cosin 077 073 073 0 30 40 50 60 70 80 90 100 10 140 160 180 00 Number of neighbors 07 1 09 08 07 06 05 04 03 0 01 0 Alha Fig5 Comarison of he effec of differen algorihms Fig6 Comarison of MAE on differen alha which oally based NN or RNN (Formula (1 (Formula (3 5 ( (1 6 alha MAE ( (3
: 1693 45 CFBIS CFBIS ( 7 ( 8, (1, NN_based;CFBIS (1~(4, CFBIS1, CFBIS,CFBIS3 CFBIS4, (3 (4,α=05 MAE 0776 0766 0756 0746 0736 076 0716 0706 0696 0 Comarison of he NN-based algorihm and CFBIS 30 NN_based CFBIS3 40 CFBIS1 CFBIS4 CFBIS 50 60 70 80 90 10010 140160 18000 Number of neighbors Fig7 Comarison of he NN-based algorihm and CFBISs (sandard cosine similariy measure MAE 093 089 085 081 077 073 Comarison of he NN-based algorihm and CFBIS NN_based CFBIS3 CFBIS1 CFBIS4 CFBIS 0 30 40 50 60 70 80 90 10010 140160 18000 Number of neighbors Fig8 Comarison of he NN-based algorihm and CFBISs (adused cosine similariy measure 7 CFBIS ( 8 CFBIS ( 7 8, CFBIS MAE, 4,,,,, 5 eb,,, CFBIS, CF, CFBIS,,CFBIS,,,,,,,, -, - [18],, References: [1] Broadvision h://wwwbroadvisioncom [] Nanooulos A, Kasaros D, Manolooulos Y A daa mining algorihm for generalized web refeching IEEE Trans on Knowledge and Daa Engineering, 003,15(5:1155 1169
1694 Journal of Sofware Vol18, No7, July 007 [3] ang S, Gao, Li JT Real ime ersona1izaion based on classificaion Chinese Journal of Comuers, 00,5(8:845 85 (in Chinese wih English absrac [4] Jin X, Zhou Y, Mobasher B A unified aroach o ersonalizaion based on robabilisic laen semanic models of eb usage and conen In: Proc of he AAAI 004 orsho on Semanic eb Personalizaion (SP 004 San Jose: AAAI, 004 6 34 h://mayacsdeauledu/~mobasher/cgi-bin/view-ubsl?cid=um [5] Herlocer J, Konsan J, Riedl J Exlaining collaboraive filering recommendaions In: Proc of he ACM 000 Conf on Comuer Suored Cooeraive or 000 41 50 h://oralacmorg/ciaioncfm?doid=358916358995 [6] Miller B, Konsan J, Terveen L, Riedl J PoceLens: Towards a ersonal recommender sysem ACM Trans on Informaion Sysems, 004,(3:437 476 [7] Baudisch P, Bruecner L TV scou: Guiding users from rined TV rogram guides o ersonalized TV recommendaion In: Proc of he nd orsho on Personalizaion in Fuure TV Malag 00 157 166 h://wwwaricbaudischcom/ublicaions/ 00-Baudisch-TV0-TVScouGuidingUsersdf [8] DeRoure D, Hall, Reich S, Hill G, Pirais A, Sairmand M MEMOIR An oen framewor for enhanced navigaion of disribued informaion Informaion Processing and Managemen Journal (Elsevier Science, 001,37(1:53 74 [9] Holmuis LE, Jacobsson M, Ros M hen media ges wise: Collaboraive filering wih mobile media agens In: Proc of he IUI 006, he 10h In l Conf on Inelligen User Inerfaces Sydney, 006 h://oralacmorg/ [10] Good N, Schafer JB, Konsan JA, Borchers A, Sarwar BM, Herlocer J, Riedl JT Combining collaboraive filering wih ersonal Agens for beer recommendaions In: Proc of he 16h Naional Conf on Arificial Inelligence (AAAI 99 Menlo Par: American Associaion for Arificial Inelligence, 1999 439 446 h://oralacmorg/ciaioncfm?id=31514931535&coll= &dl=&cfid= 15151515&CFTOKEN=6184618 [11] Jin X, Zhou YZ, Mobasher B A maximum enroy eb recommendaion sysem: Combining collaboraive and conen feaures In: Proc of he ACM SIGKDD Conf on Knowledge Discovery and Daa Mining (KDD 005 Chicago, 005 61 617 h://oralacmorg/ciaioncfm?id=1081945&dl=&coll=&cfid=15151515&cftoken=6184618 [1] Sarwar B, Karyis G, Konsan J, Riedl J Alicaion of dimensionaliy reducion in recommender sysems A case sudy In: Proc of he ebkdd 000 orsho a he ACM-SIGKDD Conf on Knowledge Discovery in Daabases (KDD 000 000 h://cieseerissuedu/sarwar00alicaionhml [13] Deng AL, Zhu YY, Shi BL A collaboraive filering recommendaion algorihm based on iem raing redicion Journal of Sofware, 003,14(9:161 168 (in Chinese wih English absrac h://wwwosorgcn/1000-985/14/161hm [14] Mobasher B, Jin X, Zhou YZ Semanically enhanced collaboraive filering on he eb In: Berend B, e al, eds eb Mining: From eb o Semanic eb LNAI 309, Sringer-Verlag, 004 57 76 [15] Sarwar B, Karyis G, Konsan J, Riedl J Iem-Based collaboraive filering recommendaion algorihms In: Proc of he 10h In l orld ide eb Conf New Yor: ACM Press, 001 85 95 [16] Korn F, Muhurishnan S Influence ses based on reverse neares neighbor ueries In: Naughon JF, Bernsein PA, eds Proc of he ACM SIGMOD In l Conf on Managemen of Daa New Yor: ACM Press, 000 01 1 [17] Herlocer J, Konsan J, Terveen L, Riedl J Evaluaing collaboraive filering recommender sysems ACM Trans on Informaion Sysems (TOIS, 004,(1:5 53 [18] Chen J, Yin J, Chen L Research on influence ses and is dynamic indexing srucure and uery algorihm based on muli-dimensional vecors Journal of Comuer Research and Develomen, 004,41(Sul:90 95 : [3],, eb,00,5(8:845 85 [13],,,003,14(9:161 168 h://wwwosorgcn/ 1000-985/14/161hm [18],,,004,41( :90 95 (1977,,,,, eb, (1968,,,,,CCF,,