In this study, a novel top-K ranking recommendation method called collaborative social metric learning (CSML) is proposed, which implements a trust network that provides both user-item and user-user interactions in simple structure. Most existing recommender systems adopting trust networks focus on item ratings, but this does not always guarantee optimal top-K ranking prediction. Conventional direct ranking systems in trust networks are based on sub-optimal correlation approaches that do not consider item-item relations. The proposed CSML algorithm utilizes the metric learning method to directly predict the top-K items in a trust network. A new triplet loss is further proposed, called sociocentric loss, which represents user-user interactions to fully exploit the information contained in a trust network, as an addition to the two commonly used triplet losses in metric learning for recommender systems, which consider user-item and item-item relations. Experimental results demonstrate that the proposed CSML outperformed existing recommender systems for real-world trust network data.
|International Journal on Semantic Web and Information Systems
|Published - 2023
Bibliographical notePublisher Copyright:
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- Item Recommendation
- Metric Learning
- Recommender Systems
- Social Recommendation
- Trust Network
- User-Item Relation
- User-User Relation
ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications