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研究報告 (2) Collaborative Filtering ー Information Filtering(2) ー. 1999 年 5 月 11 日(火) 東京工業大学 社会理工学研究科経営工学専攻 比嘉研究室 修士一年 眞崎 英彦. Collaborative Filtering is COMPUTERIZING this searching. (extracting from Berkeley Workshop on CF). What is CF?.
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研究報告(2)Collaborative FilteringーInformation Filtering(2)ー 1999年5月11日(火) 東京工業大学 社会理工学研究科経営工学専攻 比嘉研究室 修士一年 眞崎 英彦
Collaborative Filtering is COMPUTERIZINGthis searching. (extracting from Berkeley Workshop on CF) What is CF? How to Get Useful Information? Finding someone who has interests similar to yours and asking them for recommendation. Hidehiko MASAKI
Application areas • URL recommendation(WebHunter) • Music recommendation(Ringo:Firefly) • Movie recommendation(MovieLens) • Filtering out harmful information • Filtering Netnews(GroupLens) • Filtering Netnews And... CF has the potential for various fields. Hidehiko MASAKI
Netnews Architecture news sever Relay ----- ----- ----- news news Hidehiko MASAKI
Maltz’s Architecture • terrible • ok • good • great Other Vote Servers Vote File News Reader Interface Module Vote Server User Vote Database Collaborative System Existing Net News System Hidehiko MASAKI
news news Better Bit Brueau Better Bit Brueau GroupLens Architecture compatibility scalability privacy openess ease of use Hidehiko MASAKI
the way of rating rating point : 1~5 rating criteria: interest in subject quality of writing aushoritativeness of author a sample matrix of ratings id# Ken Lee Mag Nan Hidehiko MASAKI
correlation coefficent Σ Cov(K,L) (X -X) (Y -Y) i i rKL= = i σ σ Σ Σ (X -X) (Y -Y) X L i i i i prediction rKJ (J -J) Σ 6 J raters = XK + X Σ rKJ J similar calculation Hidehiko MASAKI
Ongoing Issues • Insentives and Start-up • Reliability of Predictions • Privacy Hidehiko MASAKI
Comments Using agents, automatically voting User Modeling (by system logs) Private and public sphere Hidehiko MASAKI
presented by TIT HIGA LAB Hidehiko MASAKI hide@mis.me.titech.ac.jp http://www.mis.me.titech.ac.jp/~hide/