An Iterative Method for Calculating Robust Rating Scores

Citations Over TimeTop 10% of 2014 papers

Abstract

Online rating systems are widely used to facilitate making decisions on the web. For fame or profit, people may try to manipulate such systems by posting unfair evaluations. Therefore, determining objective rating scores of products or services becomes a very important yet difficult problem. Existing solutions are mostly majority based, also employing temporal analysis and clustering techniques. However, they are still vulnerable to sophisticated collaborative attacks. In this paper we propose an iterative rating algorithm which is very robust against collusion attacks as well as random and biased raters. Unlike previous iterative methods, our method is not based on comparing submitted evaluations to an approximation of the final rating scores, and it entirely decouples credibility assessment of the cast evaluations from the ranking itself. This makes it more robust against sophisticated collusion attacks than the previous iterative filtering algorithms. We provide a rigorous proof of convergence of our algorithm based on the existence of a fixed point of a continuous mapping which also happens to be a stationary point of a constrained optimization objective. We have implemented and tested our rating method using both simulated data as well as real world movie rating data. Our tests demonstrate that our model calculates realistic rating scores even in the presence of massive collusion attacks and outperforms well-known algorithms in the area. The results of applying our algorithm on the real-world data obtained from MovieLens conforms highly with the rating scores given by Rotten Tomatoes movie critics as domain experts for movies.

Related Papers

• → A Collaborative Filtering Algorithm Based on User Similarity and Trust(2017)7 cited
• → A listwise collaborative filtering based on Plackett-Luce model(2017)4 cited
• → A Combined Forecast Scheme of User-Based and Item-based Collaborative Filtering Using Neighborhood Size(2009)
• The collaborative filtering based on co-ratings(2013)
• A Collaborative Filtering Algorithm Based on User Similarity and Trust.(2017)