Clustering user behavior patterns of E-commerce search engine-based on mixture of Markov models

Abstract

Clustering the behavior patterns of the customers is helpful to provide more specific services for E-commerce applications.A mixture model based on Markov models was proposed to solve this problem on the search engine of E-Commerce website.This model assumed that the behaviors of every customer who used the search engine can be represented by a Markov model and every user was assigned to a particular cluster randomly.Based on Bayesian Ying-Yang(BYY) harmony learning theory,a corresponding harmony function and an adaptive gradient algorithm were designed to deal with the parameter-learning and model-selection tasks.The experimental result shows that this adaptive gradient algorithm can achieve the model-selection and the parameter-learning more automatically and efficiently when compared with EM algorithm.At last,this clustering approach was applied on real-world click-through logs of the search engine on www.taobao.com and the result shows that this method can capture the nature of customers' behaviors effectively.