  Recurrent neural networks for session-based recommendation have attracted a
lot of attention recently because of their promising performance. repeat
consumption is a common phenomenon in many recommendation scenarios (e.g.,
e-commerce, music, and TV program recommendations), where the same item is
re-consumed repeatedly over time. However, no previous studies have emphasized
repeat consumption with neural networks. An effective neural approach is needed
to decide when to perform repeat recommendation. In this paper, we incorporate
a repeat-explore mechanism into neural networks and propose a new model, called
RepeatNet, with an encoder-decoder structure. RepeatNet integrates a regular
neural recommendation approach in the decoder with a new repeat recommendation
mechanism that can choose items from a user's history and recommends them at
the right time. We report on extensive experiments on three benchmark datasets.
RepeatNet outperforms state-of-the-art baselines on all three datasets in terms
of MRR and Recall. Furthermore, as the dataset size and the repeat ratio
increase, the improvements of RepeatNet over the baselines also increase, which
demonstrates its advantage in handling repeat recommendation scenarios.
