Personalizing Recommendation in Micro-blog Social Networks and E-Commerce

Abstract

Microblogs and e-commerce have emerged as two important applications of Web 2.0 technology. Service providers rely heavily on personalized recommender systems to drive sales and social interaction respectively. This thesis seeks to address the challenges of data sparsity and scalability in recommender systems, and proposes methods to improve the performance of personalized recommendation in microblog social systems and e-commerce. We first examine how the Latent Dirichlet Allocation (LDA) to find latent clusters can be applied to improve user recommendation in microblogs. We utilize the follower-followee relationship and devise an LDA based method to discover communities among the users. Next, we investigate the problem of product recommendation from the perspective that the value of a product for a user changes over time. Finally, we observe that users may have different preferences when purchasing different subsets of items, and the periods between purchases also vary from one user to another.