RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation
This addresses the need for effective repeat recommendation in domains like e-commerce and music, though it is an incremental improvement over existing neural methods.
The paper tackles the problem of repeat consumption in session-based recommendation by proposing RepeatNet, a neural model with a repeat-explore mechanism, which outperforms state-of-the-art baselines on three datasets in terms of MRR and Recall, with improvements increasing as dataset size and repeat ratio grow.
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.