Abstract
To date, there exists a variety of prediction approaches have been used in recommender systems. Among the widely known approaches are Content Based Filtering (CBF) and Collaborative Filtering (CF). Based on literatures, CF with users rating element has been widely used but the approach faced two common problems namely cold start and sparsity. As an alternative, Trust Aware Recommender Systems (TARS) for the CF based users rating has been introduced. The research progress on TARS improvement is found to be rapidly progressing but lacking in the algorithm evaluation has been started to appear. Many researchers that introduced their new TARS approach provides different evaluation of users’ views for the TARS performances. As a result, the performances of different TARS from different publications are not comparable and difficult to be analyzed. Therefore, this paper is written with objective to provide common group of the users’ views based on trusted users in TARS. Then, this paper demonstrates a comparison study between different TARS techniques with the identified common groups by means of the accuracy error, rating and users coverage. The results therefore provide a relative comparison between different TARS.
Highlights
Since the last decades, the cumulative progress of knowledge and information from the Internet technology has been tremendous
The results prove that the used of trust into Collaborative Filtering (CF) can certainly improve the prediction accuracy while maintain the fair prediction coverage
Previous evaluations on the Trust Aware Recommender System (TARS) have been conducted in different setting of parameters, which creates a difficulty for researchers to compare the performances of the different techniques
Summary
The cumulative progress of knowledge and information from the Internet technology has been tremendous. One of the popular approaches is Collaborative Filtering (CF) that utilizing user ratings based on items [1]. Cold start problem appears due to the existence of new users or items that not received any ratings [5]. As the number of items is rapidly increasing while the users rating is progressively slow, the cold start and sparsity problems would create less rating coverage and inaccurate recommendations [3]. The recommender system is called as Trust Aware Recommender System (TARS) It has been reported by many researchers that the accuracy of TARS is better than the traditional CF approach [7,8]. Since the introduction in the early of 2012, different techniques of TARS have been introduced to improve the cold start and sparsity problems. Part 4 reports the results and discussions before the concluding remarks at part 5
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