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Nov 20, 2025 · Artificial Intelligence

CoderRec: Latent Reasoning Boosts Sequential Recommendation

CoderRec, a new sequential recommendation framework jointly developed by Tencent Advertising Technology and Tsinghua University, combines domain‑specific latent reasoning with cross‑scale model collaboration to capture implicit user intent and fuse large‑language‑model semantics with traditional recommender signals, achieving state‑of‑the‑art performance on multiple Amazon datasets.

artificial intelligencecross-scale collaborationlarge language models

0 likes · 17 min read

CoderRec: Latent Reasoning Boosts Sequential Recommendation

NewBeeNLP

May 20, 2024 · Artificial Intelligence

How RecGPT Leverages ChatGPT‑Style Prompt Tuning for Better Sequential Recommendation

RecGPT applies a ChatGPT‑like pre‑training and personalized prompt‑tuning paradigm to sequential recommendation, introducing a two‑stage recall mechanism that improves offline HR/NDCG metrics and yields modest online interaction gains in a real‑world short‑video platform.

Prompt TuningRecGPTauto-regressive pretraining

0 likes · 8 min read

How RecGPT Leverages ChatGPT‑Style Prompt Tuning for Better Sequential Recommendation

AntTech

Mar 11, 2024 · Artificial Intelligence

Can Small Language Models be Good Reasoners in Recommender Systems?

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.

AILLMefficiency

0 likes · 12 min read

Can Small Language Models be Good Reasoners in Recommender Systems?

NetEase Media Technology Team

Nov 6, 2023 · Artificial Intelligence

Overview of Sequential Recommendation Models

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.

Deep LearningTransformercontrastive learning

0 likes · 17 min read

Overview of Sequential Recommendation Models

Kuaishou Tech

Mar 29, 2023 · Artificial Intelligence

ResAct: A Reinforcement Learning Approach for Long-Term User Retention in Sequential Recommendation

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.

ResActUser Retentionreinforcement learning

0 likes · 9 min read

ResAct: A Reinforcement Learning Approach for Long-Term User Retention in Sequential Recommendation

DataFunSummit

Nov 21, 2021 · Artificial Intelligence

Sequential Recommendation Algorithms: Overview and Techniques

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.

RNNTransformerattention

0 likes · 8 min read

Sequential Recommendation Algorithms: Overview and Techniques

Kuaishou Tech

Jul 7, 2021 · Artificial Intelligence

SURGE: A Graph Neural Network Based Sequential Recommendation Framework

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.

Long SequencesSURGEgraph neural networks

0 likes · 12 min read

SURGE: A Graph Neural Network Based Sequential Recommendation Framework

DataFunTalk

Aug 30, 2019 · Artificial Intelligence

TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation

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.

evaluationfactorization machinesmachine learning

0 likes · 8 min read

TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation