Description

Expected behavior with the suggested feature

• GPTop: including Global top frequent items, Personalized top frequent items, and both. https://dl.acm.org/doi/pdf/10.1145/3587153
• TIFUKNN: Modeling Personalized Item Frequency Information for Next-basket Recommendation
• Time-Dependent Next-Basket Recommendations (TIFUKNN-TA, TIFUKNN-TD)
• Beacon: Correlation-Sensitive Next-Basket Recommendation
• UP-CF@r: Recency aware collaborative filtering for next basket recommendation
• Dream: A Dynamic Recurrent Model for Next Basket Recommendation
• Sets2Sets: Learning from Sequential Sets with Neural Networks
• DNNTSP: Predicting Temporal Sets with Deep Neural Networks
• CLEA: The world is binary: Contrastive learning for denoising next basket recommendation
• M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
• Time-Aware Item Weighting for the Next Basket Recommendations
• Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping
• FPMC: Factorizing personalized markov chains for next-basket recommendation, code: https://github.com/khesui/FPMC
• Personalized Category Frequency prediction for Buy It Again recommendations
• ReCANet: A repeat consumption-aware neural network for next basket recommendation in grocery shopping
• DigBot: Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network
• BRL: Basket Representation Learning by Hypergraph Convolution on Repeated Items for Next-basket Recommendation
• MMNR: Multi-view Multi-aspect Neural Networks for Next-basket Recommendation, code https://github.com/Hiiizhy/MMNR

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