Aspect-based Sentiment Analysis for Social Recommender System

Social recommender systems provide users with a list of recommended items by exploiting knowledge from social content. Representation, similarity and ranking algorithms from the Case-Based Reasoning (CBR) community have naturally made a significant contribution to social recommender systems research [1, 2]. Recent works in social recommender systems have been focused on learning implicit preferences of users from online consumer reviews. Most online reviews contain user opinion in the form of positive and negative sentiment on multiple aspects of the product. Since a product may have multiple aspects, we hypothesize that users purchase choices are based on comparison of products; which implicitly or explicitly involves comparison of aspects of these products. Therefore, our main research question is “Does considering product aspects importance (weight) improve prediction accuracy of a product ranking algorithm?”