Abstract

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation system, WebCF-PT based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of traditional CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. A prototype recommendation system, WebCF-PT is developed and Internet shopping mall, EBIB(e-Business & Intelligence Business) is constructed to test the WebCF-PT system.

Keywords

• Collaborative Filtering;
• Recommendation System;
• Web Usage Mining;
• Product Taxonomy;
• Internet Shopping Mall