Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this paper, we propose a recommendation algorithm based on an integrated diffusion on user-item-tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations Keywords: Personalized Recommendation; Collaborative Tagging; Diffusion; Tripartite Graphs; Folksonomy The last few years have witnessed an explosion of information that the exponential growth of the Internet [1] and World Wide Web [2] confronts us with an information overload: We face too much data and sources to be able to find out those most relevant for us. Indeed, we have to make choices from thousands of movies, millions of books, billions of web pages, and so on. Evaluating all these alternatives by ourselves is not feasible at all. As a consequence, an urgent problem is how to automatically find out the relevant items for us. Internet search engine [3] , with the help of keyword-based queries, is an essential tool in getting what we want from the web. However, the search engine does not take into account personalization and returns the same results for people with far different habits. In addition, not all needs or tastes can be easily presented by keywords. Comparatively, recommender system [4], which adopts knowledge discovery techniques to provide personalized recommendations, is now considered to be the most promising way to efficiently filter out the overload information. Thus far, recommender systems have successfully found applications in e-commerce [5] , such as book recommendations in Amazon.com [6] , movie recommendations in Net°ix.com [7] , video recommendations in TiVo.com [8] , and so on.
Recently, a diffusion method REF was proposed to generate recommendations based on fusion of information diffusion on user-item and item-tag bipartite graphs.
Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs
Physica A 389 (2010) 179-186
Mathematics Computer Science