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E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation

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arxiv 2312.02443 v1 pith:JFI456JK submitted 2023-12-05 cs.IR cs.AI

Xinhang Li , Chong Chen , Xiangyu Zhao , Yong Zhang , Chunxiao Xing This is my paper · ORCID
classification cs.IRcs.AI
keywords e4sreclanguagellmsrecommendationefficiencyitemslargemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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The recent advancements in Large Language Models (LLMs) have sparked interest in harnessing their potential within recommender systems. Since LLMs are designed for natural language tasks, existing recommendation approaches have predominantly transformed recommendation tasks into open-domain natural language generation tasks. However, this approach necessitates items to possess rich semantic information, often generates out-of-range results, and suffers from notably low efficiency and limited extensibility. Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency. Nevertheless, the incapacity of LLMs to model IDs presents a formidable challenge when seeking to leverage LLMs for personalized recommendations. In this paper, we introduce an Elegant Effective Efficient Extensible solution for large language models for Sequential Recommendation (E4SRec), which seamlessly integrates LLMs with traditional recommender systems that exclusively utilize IDs to represent items. Specifically, E4SRec takes ID sequences as inputs, ensuring that the generated outputs fall within the candidate lists. Furthermore, E4SRec possesses the capability to generate the entire ranking list in a single forward process, and demands only a minimal set of pluggable parameters, which are trained for each dataset while keeping the entire LLM frozen. We substantiate the effectiveness, efficiency, and extensibility of our proposed E4SRec through comprehensive experiments conducted on four widely-used real-world datasets. The implementation code is accessible at https://github.com/HestiaSky/E4SRec/.

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Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation

    cs.IR 2026-07 accept novelty 6.5 of 10

    LBR removes length bias in LLM recommenders via length-aware attention offsets and Trie-branching information-length normalization, improving accuracy and fairness with negligible cost.

  2. Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs

    cs.IR 2025-09 conditional novelty 6.0 of 10

    MME-SID improves LLM-based sequential recommendation by fusing collaborative, text, and image embeddings with quantized semantic IDs, using MMD reconstruction and code-embedding initialization.

  3. Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning

    cs.CL 2025-07 conditional novelty 6.0 of 10

    CETRec improves LLM-based sequential recommendation by adding item-level temporal embeddings and a counterfactual tuning loss that rewards different predictions when temporal order is erased.

  4. Bidirectional Knowledge Distillation for Enhancing Sequential Recommendation with Large Language Models

    cs.IR 2025-05 conditional novelty 6.0 of 10

    An alternating distillation loop between a conventional recommender and an LLM recommender improves top-K accuracy on four datasets without adding inference-time parameters.

  5. DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation

    cs.IR 2025-05 conditional novelty 6.0 of 10

    An LLM trained by reinforcement learning to interact over multiple turns with a preference-aware recommender model outperforms both traditional and LLM-based baselines on sequential recommendation benchmarks.

  6. Diffusion Language Model for Recommendation

    cs.IR 2026-07 conditional novelty 5.0 of 10

    DLMRec adapts a masked discrete diffusion language model to recommendation via collaborative tokenization, curriculum masking, and stability-aware voting, outperforming autoregressive LLM baselines on LastFM, MovieLen...

  7. SISA-Rec: A Semantically Integrated Sequential Recommender with Contrastive Alignment

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Injecting BERT semantics into gated fusion, self-attention scores, and a contrastive objective lifts sequential recommendation HR@10 by 16–23% versus BERT4Rec on two extremely sparse Amazon datasets, especially for co...

  8. GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation

    cs.IR 2025-06 conditional novelty 4.0 of 10

    Using LLaMA-3 generated queries and dense item retrieval, GLoSS reports state-of-the-art Recall@5 on Amazon Beauty, Toys, and Sports.

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