Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender Systems

Abstract

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.