Efficient mining and prediction of user behavior patternsin mobile web systems

Vincent S. Tseng , Kawuu W. Lin

Information and Software Technology 48 (2006) 357–369

69821002 朱玉棠69821016 黃弓凌69821028 張治軍

Outline

Introduction & system architectureMining of sequential mobile access

patterns-SMAPPrediction strategiesExperimental evaluationConclusions & associated thinking

Introduction

What benefits for effectively modeling the behavior patterns of users?

To help the user get desired information in a short time

behavior patterns: a sequence of requests of a user form a location-service stream

Introduction

System architecture

SMAP-MINE:Construction of SMAP-Tree

User ID Access pattern

123456

<(a,1)(b,2)(c,5)(d,8)><(a,1)(b,3)(c,5)(d,8)><(a,3)(b,2)(d,7)><(c,6)(b,2)(d,7)><(c,8)(b,1)><(a,3)(b,6)(c,8)(d,7)>

SMAP-Tree

SR-Tree(service request tree)

SMAP-Mine algorithm

Threshold: δ (30%→6x0.3=2)

SMAP-Mine algorithm

CMAP-Mine

3

c:2

B: A:

8:2

SMAR prediction

Sequential mobile access rulesSMAR-Location SMAR-ServiceSMAR-L&S

Strength = sup * conf

( RHS = LHS * conf )

)(),)...(,)(,( 112211 mmmL lslslslR

)())...(,)(,( 2211 mm SlslslRs

),(),)...(,)(,( 2211& mmmmsL SlslslslR

SMAR prediction

Because the number of generated rules might be huge, we create a corresponding hashing tree to accelerate the access.

LHS決定 hash value RHS is calculated by

multiplying support and confidence

root

…

LHS1LHS2

RHS

SMAR prediction

SMAR-N-gram Ex1: a historical behavior is <(a,1)(b,2)(c,5) >

set n = 2, the last two pair location-services pair plus current location

now at location d, <(b,2)(c,5)(d)> as LHS

Ex2:a historical behavior is <(a,1)(b,2)(c,5) >

set n = 2, the last two pair location-services pair

<(b,2)(c,5)> as LHS

<(b,2)(c,5)(d)>

205

<(b,2)(c,5)> (e,20)(d,5)

Experimental evaluation

Probability of backward movement, Pb = 0.1 Probability of next node movement: Pn = 0.2 Probability of staying in the same node: Ps = 0.3

Experimental evaluation

Experimental evaluation

Experimental evaluation

Experimental evaluation

Conclusions & associated thinking

The proposed data mining method, namely SMAP-Mine

One physical scan on the database is needed

The prediction function : SMAR-N-gram, which is based on the N-gram model

Conclusions & associated thinking

Mining and predicting behaviors of driver for:Drunk driving Car racingetc…