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Since the inception of Recommender Systems (RS), the accuracy of therecommendations in terms of relevance has been the golden criterion forevaluating the quality of RS algorithms. However, by focusing on itemrelevance, one pays a significant price in terms of other important metrics:users get stuck in a "filter bubble" and their array of options issignificantly reduced, hence degrading the quality of the user experience andleading to churn. Recommendation, and in particular session-based/sequentialrecommendation, is a complex task with multiple - and often conflictingobjectives - that existing state-of-the-art approaches fail to address.  In this work, we take on the aforementioned challenge and introduceScalarized Multi-Objective Reinforcement Learning (SMORL) for the RS setting, anovel Reinforcement Learning (RL) framework that can effectively addressmulti-objective recommendation tasks. The proposed SMORL agent augmentsstandard recommendation models with additional RL layers that enforce it tosimultaneously satisfy three principal objectives: accuracy, diversity, andnovelty of recommendations. We integrate this framework with fourstate-of-the-art session-based recommendation models and compare it with asingle-objective RL agent that only focuses on accuracy. Our experimentalresults on two real-world datasets reveal a substantial increase in aggregatediversity, a moderate increase in accuracy, reduced repetitiveness ofrecommendations, and demonstrate the importance of reinforcing diversity andnovelty as complementary objectives.

Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning