Top-N Recommendation Based on Mutual Trust and Influence

Trust-aware Collaborative Denoising Auto-Encoder for Top-N Recommendation

Both feedback of ratings and trust relationships can be used to reveal user preference to improve recommendation performance, especially for cold users. However, the high-order correlations between tow kind of data are always ignored by existing works. Towards this problem, we propose a Correlative Denoising Autoencoder (CoDAE) model to learn correlations from both rating and trust data for Top...

Suggest * Top-n Recommendation Engine

Trust-Based Recommendation Based on Graph Similarity

Trust networks are directed weighted graphs whose nodes represent agents and edges represent trust between agents. This paper proposes a trust-based recommendation approach, which can recommend trustworthy agents to a requester in a trust network. We consider a good recommendation as one to an agent that the requester’s trusted neighbors trust highly. We relate the recommendation problem to the...

Content-Based Top-N Recommendation Using Heterogeneous Relations

Top-N recommender systems have been extensively studied. However, the sparsity of user-item activities has not been well resolved. While many hybrid systems were proposed to address the cold-start problem, the profile information has not been sufficiently leveraged. Furthermore, the heterogeneity of profiles between users and items intensifies the challenge. In this paper, we propose a content-...

A Survey on Trust Based Recommendation Systems

Recommendation systems are used to provide high quality recommendations to the users from large amount of choices. Accurate and quality recommendation is necessary in E-commerce sites. One of the most popular technique to implement a recommendation system is Collaborative Filtering (CF) [1]. It tries to find users similar to an active user and recommend him/her the items liked by these similar ...