user behavior analysis of location aware search engine third international conference of mdm, 2002...

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

2008.07.17

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 re-gard 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


Mobile Info Search

MIS is a research project conducted by NTT lab

“Personalized digital guide portal”’ site services for mo-bile 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



MIS Functionalities

Location Oriented Meta Search provides a mediation ser-vice for database-type resources

Location Oriented Robot-based Search, “kokono”, pro-vides the spatial search that documents close to a loca-tion



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.



Kokono Search



kokono Search

How to collect Local Information? Robot gathers web documents from the Internet

Parser parses the obtained documents to look up the location in-formation

(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



Kokono Search Example



Mining MIS Access Log

Site statistics

Preprocessing Remove directly accessed log, Image retrieval and Back action for

valid analysis



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



Transformation to Transaction table

Representation of access log in relational Database



Experiment Result – Association Rule Min-ing

Results of User log mining regarding Search Condition



Experiment Result – Association Rule Min-ing

Results of User log mining regarding time, location ac-quisition method



Experiment Result – Sequential Rule Mining



Conclusion

We reported the result of mining web access log of Mo-bile 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


user behavior analysis of location aware search engine third international conference of mdm, 2002...

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