A New System-Wide Diversity Measure for Recommendations with Efficient Algorithms

Abstract

Recommender systems often operate on item catalogs clustered by genres, and user bases that have natural clusterings into user types by demographic or psychographic attributes. Prior work on system-wide diversity has mainly focused on defining intent-aware metrics among such categories and maximizing relevance of the resulting recommendations, but has not combined the notions of diversity from the two point of views of items and users. In this work, (1) we introduce two new system-wide diversity metrics to simultaneously address the problems of diversifying the categories of items that each user sees, diversifying the types of users that each item is shown, and maintaining high recommendation quality. We model this as a subgraph selection problem on the bipartite graph of candidate recommendations between users and items. (2) In the case of disjoint item categories and user types, we show that the resulting problems can be solved exactly in polynomial time, by a reduction to a minimum cost flow problem. (3) In the case of non-disjoint categories and user types, we prove NP-completeness of the objective and present efficient approximation algorithms using the submodularity of the objective. (4) Finally, we validate the effectiveness of our algorithms on the MovieLens-1m and Netflix datasets, and show that algorithms designed for our objective also perform well on sales diversity metrics, and even some intent-aware diversity metrics. Our experimental results justify the validity of our new composite diversity metrics.

Optimization of Multi-stakeholder Recommender Systems for Diversity and Coverage

“…In the work [4], the authors propose system-wide diversity metrics to simultaneously achieve diversification of the categories of items that each user sees and diversification of the types of users to which each item is recommended, while maintaining high recommendation quality. In [8], diversity metrics are considered for both the items and the users, and for single-user and group recommendations.…”

Multi-Category Fairness in Sponsored Search Auctions

“…For example, the platform may like to have some ability to outsource auditing of fairness instead of taking on the responsibility of determining what constitutes fairness in each category and enforcing those determinations. 5 In this context, the platform would likely prefer to be oblivious to the particulars of fairness requirements, and instead make a guarantee that if each advertiser behaves "fairly", then the platform does not introduce any additional unfairness. With this guarantee, the platform would thus be able to leave auditing of advertiser behavior to a neutral third party or governing body.…”

“…With respect to group or statistical notions of fairness, Mehrotra et al [17] propose a mechanism for ad delivery while maintaining certain group level statistics. A variety of fairness notions have also been considered in related problems such as ranking [9,12], recommender systems [5], and news search engines [14].…”

Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines

“…Introduction. Machine learning has proved to be a powerful tool in analyzing data from different applications, such as computer vision [16], natural language processing [33], speech recognition [13], recommendation system [1,25,26], cyber security [7,35], clustering [4,29] and so on. However, some hackers can analyze the loopholes in machine learning to launch attacks on intelligent applications.…”