A Sentiment-based Similarity Model for Recommendation Systems

Recommendation Systems are tools that interpret the users' preferences in an attempt to generate fitting suggestions. Studies in this domain of research tend to conclude that the numerical user ratings are not powerful enough to truly express the users' preferences. The best way to overcome this is by extending the analysis to other elements provided by the user, such as text-based reviews of items. This data is believed to reveal a deeper understanding of the user's sentiment regarding a certain item. The goal of the proposed paper is to exploit the valuable information offered by the textual reviews, by mixing Sentiment Analysis techniques into the recommendation process. The contributions of this paper bring two major improvements to the traditional k Nearest Neighbors collaborative filtering algorithm. As a first step, a sentiment rating approach is developed based on calculated sentiment scores for each item. The resulting sentiment ratings replace the numerical ones in the recommendation process. Next, a sentiment based user similarity measure is defined taking into account three factors of similitude: the attractiveness, relevance, and popularity of reviews and users. Several experimental setups using two different datasets demonstrate that the newly proposed similarity measure outperforms some of the traditional ones and can be successfully used in the recommendation process.