A dynamic recommendation approach in online social networks

Users are strongly influenced by their friends or other users with similar interest. In this paper, we first provide a friends cluster identification approach based on the analysis of social features. Secondly, we propose our static recommendation approach based on an unbiased random walk strategy which simultaneously considers traditional recommendation approach and social relationship. Then, we further identify the change of both friendship and interest over time and propose our extended recommendation algorithm. Finally, we evaluate our approach on CiteULike and Last.fm dataset. Our experimental results demonstrate that the proposed algorithms can be very effective in recommending unknown items.