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

Collaborative Filtering versus Personal Log based Filtering:Experimental Comparison for Hotel Room SelectionRyosuke Saga and Hiroshi TsujiOsaka Prefecture University----Dongmin ShinIDS., SNU2008.07.24.

Center for E-Business Technology

Copyright 2006 by CEBT

Paper ChoosingThe reason why I chose this paperThe title of paper is interestingThe title of paper is in quite straight styleA vs BThe author should pick one method as winnerHow to utilize personal log?How to implement CF?Why is one method chosen as winner?




IndexIntroductionFeatures of TPO-goodsConsideration of recommender systemPersonal Log based filteringCollaborative filteringSimulationConclusionCenter for E-Business Technology



IntroductionRecommender systemPersonal Log based FilteringContent-basedGood for TPO-goodsCollaborative FilteringGood for non-TPO-goods (ex. CD and books, etc)Applicability to TPO-goods has not been known yet




Features of TPO-goodsSensitive to external factorsSeason, location and event related goodsThree featuresThe number of attribute is highMultiformityderived from several combinations of the attributesHigh-frequency updateThe external factors force to update attributes of TPO-goodsCenter for E-Business Technology



Consideration of recommender systemRatingIn order to recommend goods/services, recommender system should rate users preferencesExplicit ratingConsciously rated by usersImplicit ratingNot expressed by usersRecorded in database as logEx. Web visiting log, sales records, etcRates for TPO goods..Often time-variantImplicit rating is preferredAn explicit rating for goods at one TPO is not the same as for the same goods at different TPOCenter for E-Business Technology



Personal Log based filteringSales records work statistics analysisPattern resulted from the analysis is expressed as distributionPreference distributionpj(x) : preference value of the attribute j on item xRange is from 0 to 1Three search patternsHigh-angle searchfrom the most preferable area for userLow-angle searchfrom the selected goods to the preferable areaNeighbor searchAround the selected goods without preference distributionCenter for E-Business Technology



Collaborative filteringThe basic premiseSimilar users might like similar thingsThe basic processes1. To identify the similar users on their preference2. To recommend items witch they preferredSales records as Venn diagramsCenter for E-Business Technology



Collaborative filteringF-measureUsed for the measurement of retrieval performanceSame tendency of the correlation in Venn diagram

Incidentally, the recall for user a is regarded as the precision for user b




SimulationGoal of simulationComparing log based filtering with collaborative filteringSimulation environmentActual data of business hotelProvided by BestReserve Co.,Ltd10,000 users400,000 sales records160,000 room plansCriteriaGoods fitnessEvaluated 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



SimulationSimulation of CFRecommend items are not changedBecause collaborative filtering depends on the items which are bought and evaluated by other person in spite of changing the attributesAssume three casesOn season, off-season, and the other seasonThree price patternsAs corresponding to each caseThe case of highest price, the case of lowest price, the case of average priceCenter for E-Business Technology



SimulationCenter for E-Business Technology



ConclusionTPO-goods as hotel rooms have three featuresMany attributesMultiformityHigh-frequency updateWe could not use explicit rating for recommendation on TPO-goodsPersonal log based filtering is more appropriate for the hotel room selection than collaborative filteringThe accuracy of log based filtering except neighbor search kept high performanceThe accuracy of collaborative filtering was lowe3r than log based filtering and changed by TPOCenter for E-Business Technology



Paper EvaluationGood PointInteresting subject & motiveSimple & easy construction and developmentClear conclusionThey made conclusion such as formula formActual data of business web-siteBad pointFrequent mistypingEven in formulaNot fully explainedPossibly explained in other paper they wrote (access impossible)Appropriateness of criteriaCenter for E-Business Technology


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

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