FMSR: A Fairness-Aware Mobile Service Recommendation Method
Qiliang Zhu, Ao Zhou, Qibo Sun, Shangguang Wang, Fangchun Yang · 2018
With the development of mobile Internet, mobile service is emerging one after another, and the problem of information overload is becoming ever more serious. As an important tool to alleviate information overload, mobile service recommendation has attracted more and more attention. However, traditional recommendation algorithms always recommend popular services to users, which result into a rich-get-richer problem and become a barrier for the unpopular services to startup and growth. In order to promote the healthy development of the service ecosystem, it is necessary to guarantee the fairness of unpopular services. To address this problem, this paper proposes a fairness-aware mobile service recommendation method (FMSR), which gives a relatively fair recommendation opportunity for unpopular services. FMSR makes a tradeoff between recommendation accuracy and fairness, and can recommend popular services and unpopular services respectively. For unpopular services, we design a fair efficiency function and use combinatorial optimization techniques to achieve recommendations. For popular services, bias matrix factorization is utilized to implement recommendations. Experimental results based on real-world demonstrate that FMSR significantly improve the fairness of mobile service recommendation in the evolving mobile service ecosystem.