Information Filtering via Implicit Trust-based Network

TL;DR

This paper introduces a novel implicit trust-based collaborative filtering method that improves recommendation accuracy and alleviates cold start issues by leveraging user and item similarities without requiring explicit trust data.

Contribution

The paper proposes a new implicit trust measurement for user-item similarity, enhancing collaborative filtering performance and integrating user-based and item-based methods into a superior hybrid algorithm.

Findings

Enhanced recommendation accuracy on benchmark datasets

Significant reduction in cold start problem effects

Hybrid algorithm outperforms existing state-of-the-art methods

Abstract

Based on the user-item bipartite network, collaborative filtering (CF) recommender systems predict users' interests according to their history collections, which is a promising way to solve the information exploration problem. However, CF algorithm encounters cold start and sparsity problems. The trust-based CF algorithm is implemented by collecting the users' trust statements, which is time-consuming and must use users' private friendship information. In this paper, we present a novel measurement to calculate users' implicit trust-based correlation by taking into account their average ratings, rating ranges, and the number of common rated items. By applying the similar idea to the items, a item-based CF algorithm is constructed. The simulation results on three benchmark data sets show that the performances of both user-based and item-based algorithms could be enhanced greatly. Finally, a hybrid algorithm is constructed by integrating the user-based and item-based algorithms, the simulation results indicate that hybrid algorithm outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also alleviate the cold start problem.