  Since the inception of Recommender Systems (RS), the accuracy of the
recommendations in terms of relevance has been the golden criterion for
evaluating the quality of RS algorithms. However, by focusing on item
relevance, one pays a significant price in terms of other important metrics:
users get stuck in a "filter bubble" and their array of options is
significantly reduced, hence degrading the quality of the user experience and
leading to churn. Recommendation, and in particular session-based/sequential
recommendation, is a complex task with multiple - and often conflicting
objectives - that existing state-of-the-art approaches fail to address.
  In this work, we take on the aforementioned challenge and introduce
Scalarized Multi-Objective Reinforcement Learning (SMORL) for the RS setting, a
novel Reinforcement Learning (RL) framework that can effectively address
multi-objective recommendation tasks. The proposed SMORL agent augments
standard recommendation models with additional RL layers that enforce it to
simultaneously satisfy three principal objectives: accuracy, diversity, and
novelty of recommendations. We integrate this framework with four
state-of-the-art session-based recommendation models and compare it with a
single-objective RL agent that only focuses on accuracy. Our experimental
results on two real-world datasets reveal a substantial increase in aggregate
diversity, a moderate increase in accuracy, reduced repetitiveness of
recommendations, and demonstrate the importance of reinforcing diversity and
novelty as complementary objectives.
