Interest Analysis using PageRank and Social Interaction Content
Chung‐Chi Huang, Lun‐Wei Ku · 2013
We introduce a method for learning to predict reader interest. In our approach, social inter-action content and both syntactic and seman-tic features of words are utilized. The pro-posed method involves estimating topical in-terest 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 infor-mation 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 arti-cles. Moreover, results conclude that social interaction content and the proposed selection process help to accurately cover more span of reader interest.