Collaborative Filtering versus Personal Log based Filtering: Experimental Comparison for Hotel Room Selection Ryosuke Saga and Hiroshi Tsuji Osaka Prefecture

CollaborativeCollaborative Filtering versus Personal Filtering versus Personal Log based Filtering:Log based Filtering:Experimental Comparison for Hotel Experimental Comparison for Hotel Room SelectionRoom Selection

Ryosuke Saga and Hiroshi Tsuji

Osaka Prefecture University

----

Dongmin Shin

IDS., SNU

2008.07.24.

Copyright 2006 by CEBT

Paper ChoosingPaper 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


IndexIndex

Introduction

Features of TPO-goods

Consideration of recommender system

Personal Log based filtering

Collaborative filtering

Simulation

Conclusion



IntroductionIntroduction

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



Features of TPO-goodsFeatures 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



Consideration of recommender Consideration of recommender systemsystem

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



Personal Log based filteringPersonal 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



Collaborative filteringCollaborative 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



Collaborative filteringCollaborative 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



SimulationSimulation

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)




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






ConclusionConclusion

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



Paper EvaluationPaper 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


Documents

Collaborative Filtering versus Personal Log based Filtering: Experimental Comparison for Hotel Room Selection Ryosuke Saga and Hiroshi Tsuji Osaka Prefecture