Efficient mining and prediction of user behavior patterns in mobile web systems Vincent S. Tseng, Kawuu W. Lin Information and Software Technology 48 (2006) 357– 朱玉棠 黃弓凌 張治軍

Outline Introduction & system architecture Mining of sequential mobile access patterns-SMAP Prediction strategies Experimental evaluation Conclusions & 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 IDAccess pattern 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 rules SMAR-Location SMAR-Service SMAR-L&S Strength = sup * conf ( RHS = LHS * conf )

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 … LHS1 LHS2 RHS

SMAR prediction SMAR-N-gram Ex1: a historical behavior is set n = 2, the last two pair location-services pair plus current location now at location d, as LHS Ex2:a historical behavior is set n = 2, the last two pair location-services pair as LHS 20 5 (e,20) (d,5)

Experimental evaluation Probability of backward movement, P b = 0.1 Probability of next node movement: P n = 0.2 Probability of staying in the same node: P s = 0.3

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 racing etc…