Interest Analysis using PageRank and Social Interaction Content

We introduce a method for learning to predict reader interest. In our approach, social interaction content and both syntactic and semantic features of words are utilized. The proposed method involves estimating topical interest preferences and determining the informativity between articles and their social content. In interest prediction, we integrate articles’ quality social feedback representing readers’ opinions into articles to get information which may identify readers’ interests. In addition, semantic aware PageRank is used to find reader interest with the help of word interestingness scores. Evaluations show that PageRank benefits from proposed features and interest preferences inferred across articles. Moreover, results conclude that social interaction content and the proposed selection process help to accurately cover more span of reader interest.