Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches

The success of a recommender system is generally evaluated with respect to the accuracy of recommendations. However, recently diversity of recommendations has also become an important aspect in evaluating recommender systems. One dimension of diversity is called aggregate diversity, which refers to the diversity of items in the recommendation lists of all users and can be defined with different metrics. The maximization of both accuracy and the aggregate diversity simultaneously renders a multiobjective optimization problem that can be handled by different approaches. In this paper, after providing a thorough analysis of the multiobjective optimization approaches for this problem, we propose a new model that takes into account both accuracy and aggregate diversity. Different from previous works, our model is specifically designed to incorporate distributional diversity metrics, which measure how evenly the items are distributed in the recommendation lists of users. To solve the large-scale instances, we propose a column generation algorithm and a Lagrangian relaxation approach based on the decomposition of the model. We present the results of the mathematical models and the performance of the proposed methodology that are obtained by computational experiments on real-world data sets. These results reveal that our model successfully captures the trade-off between the objectives and reaches very high levels of distributional diversity.