A hearty congratulation to our former PhD student, Dr. Hao Ma for winning the rare Test of Time Award, which recognizes the significant impact his work made a decade ago in social computing that still influences work today!
Award Committee’s citation:
Recommender systems have proven themselves to be an enduring research topic in academia and industry. The paper was chosen by the committee for its importance and impact on the community. The paper looked at the deep relationship between trust and recommendation, recognizing that users don’t necessarily have similar tastes with everyone they trust, yet that trust is critical to recommendation. Authors helped establish the value of incorporating social signals into the recommender systems by identifying the most suitable social connections for different recommendation tasks. As a result, this paper has high impact (achieving by far the most citations of all the nominations for the WSDM test of time award) but also showed foresight into the importance of trust and transparency in recommendation, which has emerged as an important topic more recently.
Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.