Information Retrieval (IR) and Recommender Systems (RSs) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier in IR and RSs. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution. PDU considers fine-grained utopia points, and measures how far each point is from its utopia point, allowing to select solutions tailored to user preferences, a novel feature we call "calibration". We compare PDU against state-of-the-art strategies through extensive experiments on tasks from both IR and RS, showing that PDU combined with calibration notably impacts the solution selection.

Providing effective suggestions is of predominant importance for successful Recommender Systems (RSs). Nonetheless, the need of accounting for additional multiple objectives has become prominent, from both the final users’ and the item providers’ points of view. This need has led to a new class of RSs, called Multi-Objective Recommender Systems (MORSs). These systems are designed to provide suggestions by considering multiple (conflicting) objectives simultaneously, such as diverse, novel, and fairness-aware recommendations. In this work, we reproduce a state-of-the-art study on MORSs that exploits a reinforcement learning agent to satisfy three objectives, i.e., accuracy, diversity, and novelty of recommendations. The selected study is one of the few MORSs where the source code and datasets are released to ensure the reproducibility of the proposed approach. Interestingly, we find that some challenges arise when replicating the results of the original work, due to the nature of multiple-objective problems. We also extend the evaluation of the approach to analyze the impact of improving user-centered objectives of recommendations (i.e., diversity and novelty) in terms of algorithmic bias. To this end, we take into consideration both popularity and category of the items. We discover some interesting trends in the recommendation performance according to different evaluation metrics. In addition, we see that the multi-objective reinforcement learning approach is responsible for increasing the bias disparity in the output of the recommendation algorithm for those items belonging to positively/negatively biased categories. We publicly release datasets and codes in the following GitHub repository: https://github.com/sisinflab/MORS_reproducibility.

Although beyond-accuracy metrics have gained attention in the last decade, the accuracy of recommendations is still considered the gold standard to evaluate Recommender Systems (RSs). This approach prioritizes the accuracy of recommendations, neglecting the quality of suggestions to enhance user needs, such as diversity and novelty, as well as trustworthiness regulations in RSs for user and provider fairness. As a result, single metrics determine the success of RSs, but this approach fails to consider other criteria simultaneously. A downside of this method is that the most accurate model configuration may not excel in addressing the remaining criteria. This study seeks to broaden RS evaluation by introducing a multi-objective evaluation that considers all model configurations simultaneously under several perspectives. To achieve this, several hyper-parameter configurations of an RS model are trained, and the Pareto-optimal ones are retrieved. The Quality Indicators (QI) of Pareto frontiers, which are gaining interest in Multi-Objective Optimization research, are adapted to RSs. QI enables evaluating the model’s performance by considering various configurations and giving the same importance to each metric. The experiments show that this multi-objective evaluation overturns the ranking of performance among RSs, paving the way to revisit the evaluation approaches of the RecSys research community. We release codes and datasets in the following GitHub repository: https://github.com/sisinflab/RecMOE.

Traditional research in Recommender Systems (RSs) often solely focuses on accuracy and a limited number of beyond-accuracy dimensions. Nonetheless, real-world RSs need to consider several other aspects, such as customer satisfaction or stakeholders’ interests. Consequently, the evaluation criteria must comprehend other dimensions, like click rate, or revenue, to cite a few of them. However, what objective should the system optimize, and what objective should it sacrifice? An emerging approach to tackle the problem and aim to blend different (sometimes conflicting) objectives is Multi-Objective Recommender Systems (MORSs). This proposal sketches a strategy to exploit the Pareto optimality to introduce a new optimal solution selection approach and investigate how existing RSs perform with multi-objective tasks. The goals are twofold: (i) discovering how to rank the solutions lying on the Pareto frontier to find the best trade-off solution and (ii) comparing the Pareto frontiers of different recommendation approaches to assess whether one performs better for the considered objectives. These measures could lead to a new class of MORSs that train an RS on multiple objectives to reach the best trade-off solution directly.