1. Collaborative Filtering 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. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT Index  Introduction  Features of TPO-goods  Consideration of recommender system  Personal Log based filtering  Collaborative filtering  Simulation  Conclusion Center for E-Business Technology

4. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT Personal Log based filtering  Sales records work statistics analysis Pattern resulted from the analysis is expressed as distribution – Preference distribution – p j (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. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT Simulation Center for E-Business Technology

13. Copyright  2006 by CEBT 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. Copyright  2006 by CEBT 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