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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 multi-objective optimization problem which can be handled by different approaches. In this paper, after providing a thorough analysis of the multi-objective optimization approaches for this problem, we propose a new model which 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. In order 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.

In the current study, the details of a real estate recommender system developed for Zingat.com are discussed. The system developed is a hybrid of collaborative and content filtering approaches. Scalable methods in both the model building phase and in the recommendation list generation phase were used to work on the data set of the project (as of 2018, 300k listings and 6 million monthly sessions). This study also explained the challenges faced in developing and implementing the system, the recommendation techniques used to overcome these challenges, and the final product used for recommendation. Based on these, future recommendations are discussed.

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