Visible to the public An Adaptive Method to Learn Directive Trust Strength for Trust-Aware Recommender Systems

TitleAn Adaptive Method to Learn Directive Trust Strength for Trust-Aware Recommender Systems
Publication TypeConference Paper
Year of Publication2018
AuthorsPan, Y., He, F., Yu, H.
Conference Name2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))
ISBN Number978-1-5386-1482-2
KeywordsAdaptation models, adaptive method, Adaptive systems, cold start, Computational modeling, Data models, human factors, learn directive trust strength, learning (artificial intelligence), Mathematical model, pubcrawl, recommendation quality, recommender systems, resilience, Resiliency, Scalability, security of data, Social network services, sparse users, trust directions, trust relationships, trust-aware recommender systems, Trusted Computing, unified framework
Abstract

Trust Relationships have shown great potential to improve recommendation quality, especially for cold start and sparse users. Since each user trust their friends in different degrees, there are numbers of works been proposed to take Trust Strength into account for recommender systems. However, these methods ignore the information of trust directions between users. In this paper, we propose a novel method to adaptively learn directive trust strength to improve trust-aware recommender systems. Advancing previous works, we propose to establish direction of trust strength by modeling the implicit relationships between users with roles of trusters and trustees. Specially, under new trust strength with directions, how to compute the directive trust strength is becoming a new challenge. Therefore, we present a novel method to adaptively learn directive trust strengths in a unified framework by enforcing the trust strength into range of [0, 1] through a mapping function. Our experiments on Epinions and Ciao datasets demonstrate that the proposed algorithm can effectively outperform several state-of-art algorithms on both MAE and RMSE metrics.

URLhttps://ieeexplore.ieee.org/document/8465255
DOI10.1109/CSCWD.2018.8465255
Citation Keypan_adaptive_2018