Description

Expected behavior with the suggested feature

• ContraRec: "Sequential Recommendation with Multiple Contrast Signals" Wang et al., TOIS'2022.
• Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation
• Basket-Sensitive Personalized Item Recommendation
• Next-item recommendation with sequential hypergraphs
• A simple convolutional generative network for next item recommendation
• Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors
• Attention-based transactional context embedding for next-item recommendation
• Next-item recommendation via collaborative filtering with bidirectional item similarity
• Next item recommendation with self-attentive metric learning
• Next-item recommendations in short sessions
• Attentive sequential models of latent intent for next item recommendation
• Learning a hierarchical intent model for next-item recommendation
• Sequential modeling of hierarchical user intention and preference for next-item recommendation
• A lightweight transformer for next-item product recommendation
• Lighter and better: low-rank decomposed self-attention networks for next-item recommendation
• Dynamic multi-behavior sequence modeling for next item recommendation
• DynamicRec: a dynamic convolutional network for next item recommendation
• Online personalized next-item recommendation via long short term preference learning
• Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation
• Session-based recommendation with graph neural networks
• Repeatnet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation
• Sequential Recommender System based on Hierarchical Attention Network
• Buy It Again: Modeling Repeat Purchase Recommendations
• Personalized Category Frequency prediction for Buy It Again recommendations
• SHARE: Session-based Recommendation with Hypergraph Attention Networks
• Multi-behavior hypergraph-enhanced transformer for sequential recommendation

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