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"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit.
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation
In particular, our results show that the proposed solution is quite effective in increasing aggregate diversity and item-side utility while optimizing recommendation accuracy for end users.
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results.
Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models
In a variety of online settings involving interaction with end-users it is critical for the systems to adapt to changes in user preferences.
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems
Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system.
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks
In the second approach the HMM is used directly to generate recommendations taking into account the identified change points.
