### Description <!--- Describe your expected feature in detail --> ### Expected behavior with the suggested feature <!--- For example: --> <!--- *Adding model ABC from paper XYZ. --> - [ ] [ContraRec: "Sequential Recommendation with Multiple Contrast Signals" Wang et al., TOIS'2022.](https://github.com/THUwangcy/ReChorus/blob/TOIS22/src/models/sequential/ContraRec.py) - [ ] [Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation](https://www.dropbox.com/s/vc765ntsudo26lk/ijcai18a.pdf?dl=0) - [ ] [Basket-Sensitive Personalized Item Recommendation](https://www.dropbox.com/s/8f8djuy27z06fku/ijcai17b.pdf?dl=0) - [ ] [Next-item recommendation with sequential hypergraphs](https://dl.acm.org/doi/abs/10.1145/3397271.3401133) - [ ] [A simple convolutional generative network for next item recommendation](https://dl.acm.org/doi/abs/10.1145/3289600.3290975) - [ ] [Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors](https://dl.acm.org/doi/abs/10.1145/3219819.3220014) - [ ] [Attention-based transactional context embedding for next-item recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/11851) - [ ] [Next-item recommendation via collaborative filtering with bidirectional item similarity](https://dl.acm.org/doi/abs/10.1145/3366172) - [ ] [Next item recommendation with self-attentive metric learning](https://recnlp2019.github.io/papers/RecNLP2019_paper_21.pdf) - [ ] [Next-item recommendations in short sessions](https://dl.acm.org/doi/abs/10.1145/3460231.3474238) - [ ] [Attentive sequential models of latent intent for next item recommendation](https://dl.acm.org/doi/abs/10.1145/3366423.3380002) - [ ] [Learning a hierarchical intent model for next-item recommendation](https://dl.acm.org/doi/abs/10.1145/3473972) - [ ] [Sequential modeling of hierarchical user intention and preference for next-item recommendation](https://dl.acm.org/doi/abs/10.1145/3336191.3371840) - [ ] [A lightweight transformer for next-item product recommendation](https://dl.acm.org/doi/abs/10.1145/3523227.3547491) - [ ] [Lighter and better: low-rank decomposed self-attention networks for next-item recommendation](https://dl.acm.org/doi/abs/10.1145/3404835.3462978) - [ ] [Dynamic multi-behavior sequence modeling for next item recommendation](https://ojs.aaai.org/index.php/AAAI/article/view/25537) - [ ] [DynamicRec: a dynamic convolutional network for next item recommendation](https://dl.acm.org/doi/abs/10.1145/3340531.3412118) - [ ] [Online personalized next-item recommendation via long short term preference learning](https://link.springer.com/chapter/10.1007/978-3-319-97304-3_70) - [ ] [Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation](https://dl.acm.org/doi/abs/10.1145/3460231.3474255) - [ ] [Session-based recommendation with graph neural networks](https://ojs.aaai.org/index.php/AAAI/article/view/3804) - [ ] [Repeatnet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](https://arxiv.org/pdf/1812.02646.pdf) - [ ] [Sequential Recommender System based on Hierarchical Attention Network](https://www.ijcai.org/proceedings/2018/0546.pdf) - [ ] [Buy It Again: Modeling Repeat Purchase Recommendations](https://assets.amazon.science/40/e5/89556a6341eaa3d7dacc074ff24d/buy-it-again-modeling-repeat-purchase-recommendations.pdf) - [ ] [Personalized Category Frequency prediction for Buy It Again recommendations](https://dl.acm.org/doi/pdf/10.1145/3604915.3608822) - [ ] [SHARE: Session-based Recommendation with Hypergraph Attention Networks](https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.10) - [ ] [Multi-behavior hypergraph-enhanced transformer for sequential recommendation](https://dl.acm.org/doi/abs/10.1145/3534678.3539342) ### Other Comments
Description
Expected behavior with the suggested feature
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