A Robust Collaborative Filtering Recommendation Algorithm Based on Multidimensional Trust Model

Abstract

Collaborative filtering is one of the widely used technologies in the e-commerce recommender systems. It can predict the interests of a user based on the rating information of many other users. But the traditional collaborative filtering recommendation algorithm has the problems such as lower recommendation precision and weaker robustness. To solve these problems, in this paper we present a robust collaborative filtering recommendation algorithm based on multidimensional trust model. Firstly, according to the rating information of users, a multidimensional trust model is proposed. It measures the credibility of user’s ratings from the following three aspects: the reliability of item recommendation, the rating similarity and the user’s trustworthiness. Secondly, the computational model of trust and the traditional collaborative filtering approach are combined to select the reliable neighbor set and generate recommendation for the target user. Finally, the performances of the novel algorithm with others are compared from both sides of recommendation precision and robustness using MovieLens dataset. Compared with the existing algorithms, the proposed algorithm not only improves the quality of neighbor selection and the recommendation precision, but also has better robustness

BiNet: Trust Sub-network Extraction Using Binary Ant Colony Algorithm in Contextual Social Networks

“…Reliability: In a certain context, reliability (RLB) measures the rate of a participant's suggestions accepted by others [9]. A participant with high reliability is likely to be sought suggestions from, which can affect the trust towards the participant.…”

“…A participant with high reliability is likely to be sought suggestions from, which can affect the trust towards the participant. The reliability is calculated as one minus the deviation between the predicted rating and the actual ratings of a participant in [9].…”

Trust-based Modelling of Multi-criteria Crowdsourced Data

“…Adomavicius and Kwon [2], Bilge and Kaleli [4], Lee and Teng [24], Jhalani et al [17], Liu et al [26], Manouselis and Costopoulou [27] and Shambour et al [32] have explored the integration of multi-criteria ratings in the user profile, mainly using multimedia datasets to validate their proposals. Davoudi et al [7], Jia et al [18] and Zhang et al [37] have explored the trust modelling for rating prediction presenting trust models together with matrix factorisation algorithms or similarity metrics. However, scant research considers trust-based modelling of multicriteria crowdsourced ratings for profiling and rating prediction applied to the tourism domain in order to obtain more accurate tourism recommendations.…”

A New Approach for Trust Prediction by using collaborative filtering based of Pareto dominance in Social Networks

“…Thus, when we want to calculate the similarity between each pair of users, we must do so by only considering the items that both users have rated in common. [25] Traditional metrics display a marked tendency to show high similarity between users based on the similarity of their ratings on a very small set of items. These metrics can assign maximum similarity to two users who have each rated hundreds of items but who have only rated three items in common.…”