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We report the "Recurrent Deterioration" (RD) phenomenon observed in onlinerecommender systems. The RD phenomenon is reflected by the trend of performancedegradation when the recommendation model is always trained based on users'feedbacks of the previous recommendations. There are several reasons for therecommender systems to encounter the RD phenomenon, including the lack ofnegative training data and the evolution of users' interests, etc. Motivated totackle the problems causing the RD phenomenon, we propose the POMDP-Recframework, which is a neural-optimized Partially Observable Markov DecisionProcess algorithm for recommender systems. We show that the POMDP-Rec frameworkeffectively uses the accumulated historical data from real-world recommendersystems and automatically achieves comparable results with those modelsfine-tuned exhaustively by domain exports on public datasets.

Partially Observable Markov Decision Process for Recommender Systems