Poster Title:
A Novel Collaborative Filtering-Based Framework for Personalized Services in M-Commerce
With the rapid growth of wireless technologies and handheld devices, m-commerce is becoming a promising research area. Personalization is especially important to the success of m-commerce. This paper proposes a novel collaborative filtering-based framework for personalized services in m-commerce. The framework extends our previous works by using OLAP to represent the relationships among user, content and context information, and adopting a multi-dimensional collaborative filtering model to perform inference. It provides a powerful and well-founded mechanism to personalization for m-commerce. It is implemented in an existing m-commerce platform, and experimental results demonstrate its feasibility and correctness.