Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model

Web-page recommendation is to predict the next request of pages that Web users are potentially interested in when surfing the Web. This technique can guide Web users to find more useful pages without asking for them explicitly and has attracted much attention in the community of Web mining. However, few studies on Web page recommendation consider personalization, which is an indispensable feature to meet various preferences of users. In this paper, we propose a personalized Web page recommendation model called PIGEON (abbr. for PersonalIzed web paGe rEcommendatiON) via collaborative filtering and a topic-aware Markov model. We propose a graph-based iteration algorithm to discover users' interested topics, based on which user similarities are measured. To recommend topically coherent pages, we propose a topic-aware Markov model to learn users' navigation patterns which capture both temporal and topical relevance of pages. A thorough experimental evaluation conducted on a large real dataset demonstrates PIGEON's effectiveness and efficiency.