Trust aware recommender system with distrust in different views of trusted users

Abstract

A recommender system aims to provide users with personalized online product or service recommendations to handle the online information overload problem that keep rapidly increasing. The main problems in order to resolve the problems, one of the current trust aware mechanism that includes rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the performances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the tw Keywords: recommender system; collaborative filtering; trust aware; distrust. A recommender system aims to provide users with personalized online product or service e online information overload problem that keep rapidly increasing. The main problems in the CF recommender system are sparsity and cold start. In order to resolve the problems, one of the current researches has been directed to the CF with echanism that includes trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the erformances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the two algorithms.recommender system; collaborative filtering; trust aware; distrust. A recommender system aims to provide users with personalized online product or service e online information overload problem that keep rapidly CF recommender system are sparsity and cold start. In been directed to the CF with a trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the erformances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the o algorithms.

Leveraging Distrust Relations to Improve Bayesian Personalized Ranking

“…The improved cosine similarity is used for measuring the degree of trust and distrust. Mahtar et al [28] provided five views of trusted users, including cold start user, heavy user, opinionated user, etc. They conducted experiments on two models [1,7] with different types of view and demonstrates the effectiveness on MAE and rating coverage.…”

The implementation frameworks of meta-heuristics hybridization with dynamic parameterization

“…In the works of [13][14]9], the researchers have generally classified the meta-heuristics paradigms not to specific for the PSO and GA. The introduction of taxonomy that is specific for PSO and Differential Evolution (DE) in [19] has been found to be a worthwhile for users [34]. Due to the specific meta-heuristics, the taxonomy can be directly used as a tool to analyze the hybridization strategies between the PSO and DE.…”

An Evaluation Framework of Trust Aware Recommender System

“…Another approach is Content-Based Filtering (CBF) that uses content information of items in measuring the matching values between the items and users [2]. Additionally, demographic information, such as, age, gender and occupation, in the user profile have also been used to recommend items to the users [3][4]. Although recommender system has been widely used, some crucial problems remain appeared in the implementation for examples cold start and sparsity problems.…”

“…Furthermore, if the number of rating on the existing items is very small, the sparsity problem occurs. 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]. In order solve the problems, a recommender system with trust aware elements have been introduced [6][7].…”

“…Since the introduction in the early of 2012, different techniques of TARS have been introduced to improve the cold start and sparsity problems. One of the current techniques is TARS with distrust element [3]. It is anticipated in this research that further performance observation of TARS with distrust should be conducted as the technique is still new and current evaluation has been difficult to be compared due to less structured of performances evaluation.…”