# RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation

arXiv: https://arxiv.org/abs/1812.02646 · HTML: https://mlsanity.com/paper/1812.02646/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 30 of 51)

- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023)
- [CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://mlsanity.com/paper/2204.11067/) (2022)
- [Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation](https://mlsanity.com/paper/2107.03813/) (2021)
- [Lightweight Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/2108.11333/) (2021)
- [Rank List Sensitivity of Recommender Systems to Interaction Perturbations](https://mlsanity.com/paper/2201.12686/) (2022)
- [Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation](https://mlsanity.com/paper/2110.03996/) (2021)
- [TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics](https://mlsanity.com/paper/2502.02975/) (2025)
- [Attention Mixtures for Time-Aware Sequential Recommendation](https://mlsanity.com/paper/2304.08158/) (2023)
- [Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation](https://mlsanity.com/paper/2205.04181/) (2022)
- [M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations](https://mlsanity.com/paper/2209.11824/) (2022)
- [Unsupervised Proxy Selection for Session-based Recommender Systems](https://mlsanity.com/paper/2107.03564/) (2021)
- [Implicit Session Contexts for Next-Item Recommendations](https://mlsanity.com/paper/2208.09076/) (2022)
- [player2vec: A Language Modeling Approach to Understand Player Behavior in Games](https://mlsanity.com/paper/2404.04234/) (2024)
- [ReCODE: Modeling Repeat Consumption with Neural ODE](https://mlsanity.com/paper/2405.16550/) (2024)
- [Session-aware Linear Item-Item Models for Session-based Recommendation](https://mlsanity.com/paper/2103.16104/) (2021)
- [Introducing Self-Attention to Target Attentive Graph Neural Networks](https://mlsanity.com/paper/2107.01516/) (2021)
- [Exploiting Session Information in BERT-based Session-aware Sequential Recommendation](https://mlsanity.com/paper/2204.10851/) (2022)
- [Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation](https://mlsanity.com/paper/2408.16578/) (2024)
- [Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items](https://mlsanity.com/paper/2201.13313/) (2022)
- [ConvFormer: Revisiting Transformer for Sequential User Modeling](https://mlsanity.com/paper/2308.02925/) (2023)
- [Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions](https://mlsanity.com/paper/2504.08740/) (2025)
- [You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control](https://mlsanity.com/paper/2507.16829/) (2025)
- [Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces](https://mlsanity.com/paper/2209.05635/) (2022)
- [Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems](https://mlsanity.com/paper/2210.12940/) (2022)
- [To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders](https://mlsanity.com/paper/2310.14079/) (2023)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [PEN4Rec: Preference Evolution Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.09306/) (2021)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)

## Abstract (arXiv)

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.