Integrating Individual and Aggregate Diversity in Top-N Recommendation

Recommender systems have become one of the main components of web technologies that help people to cope with information overload. Based on the analysis of past user behavior, these systems filter items according to users’ likes and interests. Two of the most important metrics used to analyze the performance of these systems are the accuracy and diversity of the recommendation lists. Whereas all the efforts exerted in the prediction of the user interests aim at maximizing the former, the latter emerges in various forms, such as diversity in the lists across all user recommendation lists, referred to as aggregate diversity, and diversity in the lists of individuals, known as individual diversity. In this paper, we tackle the combination of these three objectives and justify this approach by showing through experiments that handling these objectives in pairs does not yield satisfactory results in the third one. To that end, we develop a mathematical model that is formulated using multiobjective optimization approaches. To cope with the intractability of this nonlinear integer programming model, its special structure is exploited by a decomposition technique. For the solution of the resulting formulation, we propose an iterative framework that is composed of a clique-generating genetic algorithm, a constructive heuristic, and an improvement heuristic. The former is designed to incorporate all objective functions into the generated cliques and specifically impose a certain level of individual diversity, whereas the latter chooses one clique for each user such that the desired aggregate diversity level is fulfilled. We conduct experiments on three data sets and show that the proposed modeling approach successfully handles all objectives according to the needs of the system and that the proposed methodology is capable of yielding good upper bounds.