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