Content-based personalised recommendation in virtual shopping environment

In our paper, we illustrate two kinds of product recommender algorithms to support e-commerce. For those commodities which a consumer seldom buys, user-rating methods are required to acquire the data set of the products rating in terms of the preference of the specific user. Thus, the combination of the Genetic Algorithm (GA) and k nearest neighbour method is proposed to infer the customer's personal preferences from rated products. On the other hand, for products that the consumers often buy, an interactive mode is provided for the users to evaluate the degree of interest for each feature of the products. We finally incorporate an intelligent agent model into the virtual shopping mall, which makes it easy for customers to fuse into the shopping experience.

[1]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[2]  Ralph Bergmann,et al.  Intelligent Customer Support for Product Selection with Case-Based Reasoning , 2002 .

[3]  Xindong Wu,et al.  Support vector machines based on K-means clustering for real-time business intelligence systems , 2005, Int. J. Bus. Intell. Data Min..

[4]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[5]  Nikos I. Karacapilidis,et al.  A hybrid framework for similarity-based recommendations , 2005, Int. J. Bus. Intell. Data Min..

[6]  Henrik Stormer,et al.  Personalized Websites for Mobile Devices using Dynamic Cascading Style Sheets , 2005, Int. J. Web Inf. Syst..

[7]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[8]  Pattie Maes,et al.  Agents that buy and sell , 1999, CACM.

[9]  Thorsten Joachims,et al.  Web Watcher: A Tour Guide for the World Wide Web , 1997, IJCAI.

[10]  Arbee L. P. Chen,et al.  A music recommendation system based on music data grouping and user interests , 2001, CIKM '01.

[11]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[12]  Chi-Hoon Lee,et al.  Web personalization expert with combining collaborative filtering and association rule mining technique , 2001, Expert Syst. Appl..

[13]  Bo Yang,et al.  Similarity-based clustering strategy for mobile ad hoc multimedia databases , 2005, Mob. Inf. Syst..

[14]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[15]  Chih-Hung Liu,et al.  Intelligent agent-based systems for personalized recommendations in Internet commerce , 2002, Expert Syst. Appl..

[16]  Yannis Manolopoulos,et al.  Evaluation of similarity searching methods for music data in P2P networks , 2005, Int. J. Bus. Intell. Data Min..

[17]  Maytham Safar,et al.  K nearest neighbor search in navigation systems , 2005, Mob. Inf. Syst..

[18]  Kristian J. Hammond,et al.  The FindMe Approach to Assisted Browsing , 1997, IEEE Expert.

[19]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[20]  Philip S. Yu,et al.  Horting hatches an egg: a new graph-theoretic approach to collaborative filtering , 1999, KDD '99.

[21]  Sandip Sen,et al.  A buyer's agent , 2000, AGENTS '00.

[22]  Vladimir Kotlyar,et al.  Personalization of Supermarket Product Recommendations , 2004, Data Mining and Knowledge Discovery.

[23]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[24]  Siu Cheung Hui,et al.  A Web Usage Lattice Based Mining Approach for Intelligent Web Personalization , 2005, Int. J. Web Inf. Syst..

[25]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[26]  Xiaohui Liu,et al.  Data mining from 1994 to 2004: an application-orientated review , 2005, Int. J. Bus. Intell. Data Min..

[27]  Kate Smith-Miles,et al.  Kernal Width Selection for SVM Classification: A Meta-Learning Approach , 2005, Int. J. Data Warehous. Min..

[28]  Bruce Krulwich,et al.  The InfoFinder Agent: Learning User Interests through Heuristic Phrase Extraction , 1997, IEEE Expert.

[29]  Ning Zhong,et al.  Online recommendation based on customer shopping model in e-commerce , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[30]  Kate Smith-Miles,et al.  A clustering algorithm based on an estimated distribution model , 2005, Int. J. Bus. Intell. Data Min..