Remembering past and predicting future: a hybrid recurrent neural network based recommender system

Traditional recommender systems (RS) assume users' taste to be static (taste remains same over time) and reactive (a change in taste cannot be predicted and is observed only after it occurs). Further, traditional RS restricts the recommendation process to candidate items generation. This work aims to explore two phases of RS, i.e., Candidate Generation as well as Candidate Ranking. We propose a RS from a multi-objective (short-term prediction, long-term prediction, diversity, and popularity bias) perspective which was previously overlooked. The sequential and non-sequential behavior of users is exploited to predict future behavioral trajectories with the consideration of short-term and long-term prediction using recurrent neural networks and nearest neighbors approach. Further, a novel candidate ranking method is introduced to prevent users from being entangled in recommended items. On multiple datasets, largest being MovieLens (ML) 1M, our model shows excellent results achieving a hit rate and short-term prediction success of 58% and 71% respectively on ML 1M. Further, it implicitly handles two important parameters, i.e., diversity and item popularity with a success rate of 59.22% and 34.28% respectively.