T&TRS: robust collaborative filtering recommender systems against attacks

In recent years, the Internet has had a main and important contribution to human life and the amount of data on the World Wide Web such as books, movies, videos and, etc. increase rapidly. Recommender systems allow users to quickly access items that are closer to their interests. One of the most popular and easiest models of recommender systems is the Collaborative Filtering (CF) model, which uses the items ratings given by users. The important challenge of CF is robust against the attacks which manipulated by fake users to reduce the efficiency of the system. Therefore, the impact of attacks on the item recommendations will increase and fake items will be easily recommended to users. The purpose of this paper is to design a robust CF recommender system, T&TRS, Time and Trust Recommender System, against user attacks. Our proposed system improves the performance of users clustering for detecting the fake users based on a novel community detection algorithm that is introduced in this paper. Our proposed system calculates the reliably value for all items ratings and tags them as suspicious or correct. T&TRS considered the rating time and implicit and explicit trust among users for constructing the weighted user-user network and detects communities as the nearest neighbors of the users to predict unknown items ratings. After detecting the suspect users and items using a novel community detection method, our proposed system removes them from rating matrix and predict the rating of unobserved items and generate the Top@k items according to the user interests. We inject the random and average attacks into the Epinion data set and evaluate our proposed systems based on Precision, Recall, F1, MAE, RMSE, and RC measures before and after attacks. The experimental results indicated that the precision of items recommendations increase after attack detection and show the effectiveness of T&TRS in comparison to the two base K-means methods such as KMCF-U, KMCF-I and graph-based methods such as TRACCF, and TOTAR.