User Behavior Analysis of Location Aware Search Engine Third international Conference of MDM, 2002 Takahiko Shintani, Iko Pramudiono NTT Information Sharing Platform Lab. Summarized by 공기현

Copyright  2006 by CEBT Introduction  Access log of a web site records every user requests  From the Access log, we can know Which pages were visited by the user What kind of Requests submitted Where the user come from  This paper focus on mining the behavior of user with regard to his location from user access log  We use association rule mining and sequential pattern mining for user log analysis Association rule mining Sequential pattern Mining IDS Lab. Seminar - 2Center for E-Business Technology

Copyright  2006 by CEBT Mobile Info Search  MIS is a research project conducted by NTT lab  “Personalized digital guide portal”’ site services for mobile user  Provides location aware information from the internet by collecting, structuring, organizing and filtering  Between Users and information sources, MIS mediates database type resources such as online maps, internet “yellow-pages”  Authors collect user logs from MIS site IDS Lab. Seminar - 3Center for E-Business Technology

Copyright  2006 by CEBT MIS Functionalities  Location Oriented Meta Search provides a mediation service for database-type resources  Location Oriented Robot-based Search, “kokono”, provides the spatial search that documents close to a location IDS Lab. Seminar - 4Center for E-Business Technology

Copyright  2006 by CEBT User Location Acquisition  The user location represents the geographical position, or the area of the information in the form of address strings (latitude, landmarks,…)  The user location is automatically obtained by Mobile Device such as GPS, PDA, Notebook  In this paper, we use PHS system and its Logs PHS use many small base stations The base stations are placed in almost every stations, buildings, and street. – User Location accuracy is better than Cell phone. IDS Lab. Seminar - 5Center for E-Business Technology

Copyright  2006 by CEBT Kokono Search IDS Lab. Seminar - 6Center for E-Business Technology

Copyright  2006 by CEBT kokono Search  How to collect Local Information? Robot gathers web documents from the Internet Parser parses the obtained documents to look up the location information (address) and spatial information(longitude-latitude) Store web documents with local information to repository  How to structure the Local Information? Divide document into morphemes by the parser Compare noun phrase to the address dictionary and regard it as an address if it satisfies the following condition – Any address strings without upper address – Cities with address suffix (ex. Yokohama Shi) – Towns or block numbers with the city name – Block IDS Lab. Seminar - 7Center for E-Business Technology

Copyright  2006 by CEBT Kokono Search Example IDS Lab. Seminar - 8Center for E-Business Technology

Copyright  2006 by CEBT Mining MIS Access Log  Site statistics  Preprocessing Remove directly accessed log, Image retrieval and Back action for valid analysis IDS Lab. Seminar - 9Center for E-Business Technology

Copyright  2006 by CEBT Access Log Format  Each search log consists Web CGI parameters Location information (Address, station, zip, …) Location acquisition method ( from) Resource type (submit) Name of resource to search form ( shop, map, rail, station..) Condition of search Access Hour, Access Date IDS Lab. Seminar - 10Center for E-Business Technology

Copyright  2006 by CEBT Transformation to Transaction table  Representation of access log in relational Database IDS Lab. Seminar - 11Center for E-Business Technology

Copyright  2006 by CEBT Experiment Result – Association Rule Mining  Results of User log mining regarding Search Condition IDS Lab. Seminar - 12Center for E-Business Technology

Copyright  2006 by CEBT Experiment Result – Association Rule Mining  Results of User log mining regarding time, location acquisition method IDS Lab. Seminar - 13Center for E-Business Technology

Copyright  2006 by CEBT Experiment Result – Sequential Rule Mining IDS Lab. Seminar - 14Center for E-Business Technology

Copyright  2006 by CEBT Conclusion  We reported the result of mining web access log of Mobile Info Search  We use two techniques, the association rule mining and sequential pattern mining  Using those two techniques, we can figure out how the behavior of MIS user and services they use are affected by their location  Unfortunately, there are many case when the user is overwhelmed by so many result Clustering the search results on their contents is required IDS Lab. Seminar - 15Center for E-Business Technology