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Generative Sequential Recommendation with GPTRec

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arxiv 2306.11114 v1 pith:5EZ5L3DV submitted 2023-06-19 cs.IR

Aleksandr V. Petrov , Craig Macdonald This is my paper · ORCID
classification cs.IR
keywords recommendationmodelsgptrecitemlanguagesequentialgenerativenext-k
verification ladder T0 review T1 audit T2 compute T3 formal
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Sequential recommendation is an important recommendation task that aims to predict the next item in a sequence. Recently, adaptations of language models, particularly Transformer-based models such as SASRec and BERT4Rec, have achieved state-of-the-art results in sequential recommendation. In these models, item ids replace tokens in the original language models. However, this approach has limitations. First, the vocabulary of item ids may be many times larger than in language models. Second, the classical Top-K recommendation approach used by these models may not be optimal for complex recommendation objectives, including auxiliary objectives such as diversity, coverage or coherence. Recent progress in generative language models inspires us to revisit generative approaches to address these challenges. This paper presents the GPTRec sequential recommendation model, which is based on the GPT-2 architecture. GPTRec can address large vocabulary issues by splitting item ids into sub-id tokens using a novel SVD Tokenisation algorithm based on quantised item embeddings from an SVD decomposition of the user-item interaction matrix. The paper also presents a novel Next-K recommendation strategy, which generates recommendations item-by-item, considering already recommended items. The Next-K strategy can be used for producing complex interdependent recommendation lists. We experiment with GPTRec on the MovieLens-1M dataset and show that using sub-item tokenisation GPTRec can match the quality of SASRec while reducing the embedding table by 40%. We also show that the recommendations generated by GPTRec on MovieLens-1M using the Next-K recommendation strategy match the quality of SASRec in terms of NDCG@10, meaning that the model can serve as a strong starting point for future research.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 8 citations worldwide. Full citation record

  1. VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

    cs.IR 2026-07 conditional novelty 6.0 of 10

    VaLiDRec constructs variable-length item IDs from native LLM vocabulary tokens and predicts them in parallel, outperforming fixed-code semantic-ID recommenders on four Amazon datasets.

  2. DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation

    cs.LG 2026-02 conditional novelty 6.0 of 10

    Using LLM-mined 'deep interests' as semantic IDs and as a reinforcement-learning reward gives reported 9-15% relative HR/NDCG gains in sequential recommendation, though the paper lacks code, error bars, and a cross-do...

  3. RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation

    cs.IR 2025-09 conditional novelty 6.0 of 10

    A from-scratch model that tokenizes items into hierarchical codes and predicts next-item codes reaches higher average zero-shot AUC on 8 datasets than LLM recommenders up to 7B parameters.

  4. LLM4MEA: Data-free Model Extraction Attacks on Sequential Recommenders via Large Language Models

    cs.IR 2025-07 conditional novelty 6.0 of 10

    An LLM-driven agent generates synthetic interaction sequences that, when queried against a target sequential recommender, produce surrogate models with higher agreement to the target than random or autoregressive data...

  5. GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A generative recommendation model that adds chain-of-thought attribute tokens from a product knowledge graph and uses journey-aware sparse attention, reporting large accuracy and efficiency gains over prior generative...

  6. RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation

    cs.IR 2025-07 conditional novelty 6.0 of 10

    A reimplementation of RecRanker shows its pointwise variant's high top-k scores come from ground-truth data leakage in the prompts, and the new RecRankerEval framework finds listwise tuning, DBSCAN sampling, XSimGCL, ...

  7. Generating Long Semantic IDs in Parallel for Recommendation

    cs.IR 2025-06 conditional novelty 6.0 of 10

    RPG replaces autoregressive semantic ID generation with parallel multi-token prediction plus graph-constrained decoding, improving NDCG@10 by about 12.6% over generative baselines while keeping inference cost independ...

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