A Collaborative Filtering Recommendation Algorithm Based on Influence Sets

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1 ISSN , CODEN RUXUE Journal of Sofware, Vol18, No7, July 007, h://wwwosorgcn DOI: /os Tel/Fax: by Journal of Sofware All righs reserved 1+, 1 (, (, 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 , China (Dearmen of Comuer Science, Sun Ya-Se Universiy, Guangzhou 51075, China + Corresonding auhor: Phn: , Fax: , ellachen@scueducn, h://wwwscueducn Chen J, Yin J A collaboraive filering recommendaion algorihm based on influence ses Journal of Sofware, 007,18(7: h://wwwosorgcn/ /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 Nos , ( ; 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 No ( ; he Naural Science Foundaion of Guangdong Province of China under Gran Nos050030, ( ; he Research Foundaion of Science and Technology Plan Proec in Guangdong Province of China under Gran No005B ( ; he Naural Science Foundaion of Souh China Universiy of Technology under Gran NoB07E ( Received ; Acceed

2 1686 Journal of Sofware Vol18, No7, July 007,, : ; ; : TP393 : A eb, eb, eb, eb,eb eb eb eb eb,,,, (recommendaion sysem,, [1] [,3] [4] [5], (collaboraive filering 1,,,, N (o N recommendaion, (user-based collaboraive filering, :PoceLens( GrouLens [6] Usene ;TV Scou [7] eb ;Memior [8] ;Push!Music [9], : Good [10] ;Jin [11] ; Sarwar [1],, : 1 [1], NN,, [13],, ;,,,,,, 3 (new iem roblem [14],,,, (iem-based collaboraive filering [15], O(n,,n :a, ;b

3 : 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

4 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 (

5 : , 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,

6 1690 Journal of Sofware Vol18, No7, July , 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

7 : 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 , 3 500, ,, 0 1~5,5 erfec, 1 bad,, x,,x=08 80%, 0%, x=08,,, ψ, ψ = 1 = , 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

8 169 Journal of Sofware Vol18, No7, July Comarison of similariy measure mehods Cosin Adused cosin Correlaion MAE Number of neighbors Fig4 Comarison of similariy measure mehods , 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 Comarison of he effec of differen iemses Cosin (NN_based Cosin (RNN_based Adused cosin (NN_based Adused cosin (RNN_based MAE Comarisons of MAE on differen alha Cosin Adused cosin Number of neighbors 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

9 : CFBIS CFBIS ( 7 ( 8, (1, NN_based;CFBIS (1~(4, CFBIS1, CFBIS,CFBIS3 CFBIS4, (3 (4,α=05 MAE Comarison of he NN-based algorihm and CFBIS 30 NN_based CFBIS3 40 CFBIS1 CFBIS4 CFBIS Number of neighbors Fig7 Comarison of he NN-based algorihm and CFBISs (sandard cosine similariy measure MAE Comarison of he NN-based algorihm and CFBIS NN_based CFBIS3 CFBIS1 CFBIS4 CFBIS 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:

10 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: (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, 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 h://oralacmorg/ciaioncfm?doid= [6] Miller B, Konsan J, Terveen L, Riedl J PoceLens: Towards a ersonal recommender sysem ACM Trans on Informaion Sysems, 004,(3: [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 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, h://oralacmorg/ciaioncfm?id= &coll= &dl=&cfid= &CFTOKEN= [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, h://oralacmorg/ciaioncfm?id= &dl=&coll=&cfid= &cftoken= [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 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: (in Chinese wih English absrac h://wwwosorgcn/ /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, [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, [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, [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: [13],,,003,14(9: h://wwwosorgcn/ /14/161hm [18],,,004,41( :90 95 (1977,,,,, eb, (1968,,,,,CCF,,

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