Dynamic collaborative recurrent learning

Abstract

In this paper, we provide a unified learning algorithm, dynamic collaborative recurrent learning, DCRL, of two directions of recommendations: temporal recommendations focusing on tracking the evolution of users' long-term preference and sequential recommendations focusing on capturing short-term preferences given a short time window. Our DCRL builds based on RNN and Sate Space Model (SSM), and thus it is not only able to collaboratively capture users' short-term and long-term preferences as in sequential recommendations, but also can dynamically track the evolution of users' long-term preferences as in temporal recommendations in a unified framework. In addition, we introduce two smoothing and filtering scalable inference algorithms for DCRL's offline and online learning, respectively, based on amortized variational inference, allowing us to effectively train the model jointly over all time. Experiments demonstrate DCRL outperforms the temporal and sequential recommender models, and does capture users' short-term preferences and track the evolution of long-term preferences.