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This article presents SLIM, a knowledge‑distillation framework that transfers the reasoning abilities of large language models to compact models for sequential recommendation, enhancing item representation, user profiling, and bias mitigation while achieving comparable performance with far lower computational resources.
The article surveys sequential recommendation models from early non-deep approaches like FPMC, through RNN-based GRU4Rec and CNN-based Caser, to Transformer-based methods such as SASRec, BERT4Rec, TiSASRec, and recent contrastive-learning techniques, recommending SASRec or its variants for production use.
The paper introduces ResAct, a reinforcement‑learning framework that improves long‑term user retention in sequential recommendation by constraining the policy space near the online‑serving policy and employing a conditional variational auto‑encoder, residual actor, and state‑action value network, achieving significant gains over existing methods on a large‑scale short‑video dataset.
This article surveys sequential recommendation methods, covering standard models such as pooling, RNN, CNN, attention, and Transformer, as well as long‑short term, multi‑interest, multi‑behavior approaches, and recent advances like contrastive learning, highlighting their impact on recommendation performance.
The SURGE framework leverages graph neural networks to construct and pool interest graphs from user interaction sequences, achieving stable and fast convergence, robust long‑sequence modeling, and significant performance gains over existing sequential recommendation methods on e‑commerce and short‑video datasets.
This article reviews the TransFM model, which combines the translation‑based sequential recommendation approach (TransRec) with factorization machines (FM), explains its formulation, optimization via sequential Bayesian personalized ranking, and demonstrates its superior performance on Amazon and Google Local datasets compared with several baselines.