Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification

In this paper we aim to resolve the recommendation problem by using the virtual ratings in online environments when user rating information is not available. As a matter of fact, in most of current websites especially the Chinese video-sharing ones, the traditional pure rating based collaborative filtering recommender methods are not fully qualified due to the sparsity of rating data. Motivated by our prior work on the investigation of user reviews that broadly appear in such sites, we hence propose a new recommender algorithm by fusing a self-supervised emoticon-integrated sentiment classification approach, by which the missing User-Item Rating Matrix can be substituted by the virtual ratings which are predicted by decomposing user reviews as given to the items. To test the algorithm's practical value, we have first identified the self-supervised sentiment classification's higher performance by comparing it with a supervised approach. Moreover, we conducted a statistic evaluation method to show the effectiveness of our recommender system on improving Chinese online video recommendations' accuracy.