Learning to blend vitality rankings from heterogeneous social networks
Heterogeneous social network services, such as Facebook and Twitter, have emerged as popular, and often effective channels for Web users to capture updates from their friends. The explosion in popularity of these social network services, however, has created the problem of “information overload”. The problem is becoming more severe as more and more users have engaged in more than one social networks simultaneously, each of which usually yields different friend connections and various sources of updates. Thus, it has made necessity to perform effective information filtering to retrieve information really attractive to web users from each of social networks and further blend them into a unified ranking list. In this paper, we introduce the problem of blending vitality rankings from heterogeneous social networks, where vitality denotes all kinds of updates user receives in various social networks. We propose a variety of content, users, and users correlation features for this task. Since vitalities from different social networks are likely to have different sets of features, we employ a divide-and-conquer strategy in order to fully exploit all available features for vitalities from each social network, respectively. Our experimental results, obtained from a large scale evaluation over two popular social networks, demonstrate the effectiveness of our method for putting vitalities that really interest users into higher orders in the blended ranking list. We complement our results with a thorough investigation of the feature importance and model selection with respect to both blending strategy and ranking for each social network.