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Unifying Generative and Dense Retrieval for Sequential Recommendation

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arxiv 2411.18814 v2 pith:5Y3QNJQR submitted 2024-11-27 cs.IR cs.AI

Liu Yang , Fabian Paischer , Kaveh Hassani , Jiacheng Li , Shuai Shao , Zhang Gabriel Li , Yun He , Xue Feng
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Nima Noorshams Sem Park Bo Long Robert D Nowak Xiaoli Gao Hamid Eghbalzadeh
This is my paper · ORCID
classification cs.IRcs.AI
keywords retrievalgenerativedenseitemsequentialitemsrecommendationuser
verification ladder T0 review T1 audit T2 compute T3 formal
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Sequential dense retrieval models utilize advanced sequence learning techniques to compute item and user representations, which are then used to rank relevant items for a user through inner product computation between the user and all item representations. However, this approach requires storing a unique representation for each item, resulting in significant memory requirements as the number of items grow. In contrast, the recently proposed generative retrieval paradigm offers a promising alternative by directly predicting item indices using a generative model trained on semantic IDs that encapsulate items' semantic information. Despite its potential for large-scale applications, a comprehensive comparison between generative retrieval and sequential dense retrieval under fair conditions is still lacking, leaving open questions regarding performance, and computation trade-offs. To address this, we compare these two approaches under controlled conditions on academic benchmarks and propose LIGER (LeveragIng dense retrieval for GEnerative Retrieval), a hybrid model that combines the strengths of these two widely used methods. LIGER integrates sequential dense retrieval into generative retrieval, mitigating performance differences and enhancing cold-start item recommendation in the datasets evaluated. This hybrid approach provides insights into the trade-offs between these approaches and demonstrates improvements in efficiency and effectiveness for recommendation systems in small-scale benchmarks.

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Cited by 8 Pith papers

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    A feedback-grounded framework discovers recommendation policies by their measured advantage over intent-only baselines and distills them into two latent tokens of a lightweight Semantic-ID recommender.

  2. Generative Recommendation with Semantic IDs: A Practitioner's Handbook

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    An open-source framework and ablation study showing which semantic-ID generative recommendation components actually matter, with results that challenge several standard defaults.

  3. Generating Long Semantic IDs in Parallel for Recommendation

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    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...

  4. Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders

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    A survey and benchmark of LLM recommenders finds that augmenting LLMs with non-LLM techniques (semantic IDs, collaborative signals) generally improves sequential recommendation accuracy on Amazon'23.

  5. Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs

    cs.IR 2026-07 conditional novelty 5.0 of 10

    Interleaving fixed log-scale gap tokens with semantic IDs, plus TA-FAMAE temporal regularization, consistently beats ReSID and other SID generative baselines on Amazon sequential recommendation.

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    cs.IR 2026-04 conditional novelty 5.0 of 10

    Semantic IDs with STE-trained multi-modal RQ-VAE and heuristic collision resolution improve Snapchat ranking and generative retrieval offline and in online A/B tests.

  7. FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets

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  8. GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models

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