Visualizing Recommendation Flow on Social Network

In contrast with centralized recommender systems, social recommendation algorithm is applied to the item rating data on social networks. Meaningful recommendation can be uncovered by the topology of social network as well as the similarity between users. More importantly, this information becomes propagated into the users in the estimated same groups. As the goal of this paper, we propose a novel method for visual explanation of the recommender system on social network. For experiments, we simulate the recommendation flow by using the MovieLens dataset on a social network constructed with FOAF.