Ryosuke Saga and Hiroshi Tsuji Osaka Prefecture University ---- Dongmin Shin IDS., SNU 2008.07.24.

1. CollaborativeFiltering versus Personal Log based Filtering:Experimental Comparison for Hotel Room Selection Ryosuke Saga and Hiroshi Tsuji Osaka Prefecture University ---- Dongmin Shin IDS., SNU 2008.07.24.

2. Paper Choosing • The reason why I chose this paper • The title of paper is interesting • The title of paper is in quite straight style • A vs B • The author should pick one method as winner • How to utilize personal log? • How to implement CF? • Why is one method chosen as winner? Center for E-Business Technology

3. Index • Introduction • Features of TPO-goods • Consideration of recommender system • Personal Log based filtering • Collaborative filtering • Simulation • Conclusion Center for E-Business Technology

4. Introduction • Recommender system • Personal Log based Filtering • Content-based • Good for TPO-goods • Collaborative Filtering • Good for non-TPO-goods (ex. CD and books, etc) • Applicability to TPO-goods has not been known yet Center for E-Business Technology

5. Features of TPO-goods • Sensitive to external factors • Season, location and event related goods • Three features • The number of attribute is high • Multiformity • derived from several combinations of the attributes • High-frequency update • The external factors force to update attributes of TPO-goods Center for E-Business Technology

6. Consideration of recommender system • Rating • In order to recommend goods/services, recommender system should rate user’s preferences • Explicit rating • Consciously rated by users • Implicit rating • Not expressed by users • Recorded in database as log • Ex. Web visiting log, sales records, etc • Rates for TPO goods.. • Often time-variant • Implicit rating is preferred • An explicit rating for goods at one TPO is not the same as for the same goods at different TPO Center for E-Business Technology

7. Personal Log based filtering • Sales records work statistics analysis • Pattern resulted from the analysis is expressed as distribution • Preference distribution • pj(x) : preference value of the attribute j on item x • Range is from 0 to 1 • Three search patterns • High-angle search • from the most preferable area for user • Low-angle search • from the selected goods to the preferable area • Neighbor search • Around the selected goods without preference distribution Center for E-Business Technology

8. Collaborative filtering • The basic premise • Similar users might like similar things • The basic processes 1. To identify the similar users on their preference 2. To recommend items witch they preferred • Sales records as Venn diagrams Center for E-Business Technology

9. Collaborative filtering • F-measure • Used for the measurement of retrieval performance • Same tendency of the correlation in Venn diagram • Incidentally, the recall for user a is regarded as the precision for user b Center for E-Business Technology

10. Simulation • Goal of simulation • Comparing log based filtering with collaborative filtering • Simulation environment • Actual data of business hotel • Provided by BestReserve Co.,Ltd • 10,000 users • 400,000 sales records • 160,000 room plans • Criteria • Goods fitness • Evaluated value based on the preference extracted sales records • K : set of attributes (price, room size, distance from mass transit and breakfast service) Center for E-Business Technology

11. Simulation • Simulation of CF • Recommend items are not changed • Because collaborative filtering depends on the items which are bought and evaluated by other person in spite of changing the attributes • Assume three cases • On season, off-season, and the other season • Three price patterns • As corresponding to each case • The case of highest price, the case of lowest price, the case of average price Center for E-Business Technology

12. Simulation Center for E-Business Technology

13. Conclusion • TPO-goods as hotel rooms have three features • Many attributes • Multiformity • High-frequency update • We could not use explicit rating for recommendation on TPO-goods • Personal log based filtering is more appropriate for the hotel room selection than collaborative filtering • The accuracy of log based filtering except neighbor search kept high performance • The accuracy of collaborative filtering was lowe3r than log based filtering and changed by TPO Center for E-Business Technology

14. Paper Evaluation • Good Point • Interesting subject & motive • Simple & easy construction and development • Clear conclusion • They made conclusion such as formula form • Actual data of business web-site • Bad point • Frequent mistyping • Even in formula • Not fully explained • Possibly explained in other paper they wrote (access impossible) • Appropriateness of criteria Center for E-Business Technology