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GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation

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arxiv 2304.03879 v1 pith:YSSH7EQN submitted 2023-04-08 cs.IR cs.LG

Jinming Li , Wentao Zhang , Tian Wang , Guanglei Xiong , Alan Lu , Gerard Medioni This is my paper
classification cs.IRcs.LG
keywords useritemssearchframeworkinterestslanguagequeriesdiversity
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capabilities of NLP models; (2) interpreting user interests to improve relevance and diversity; and (3) adapting practical circumstances such as growing item inventories. To address these limitations, we present GPT4Rec, a novel and flexible generative framework inspired by search engines. It first generates hypothetical "search queries" given item titles in a user's history, and then retrieves items for recommendation by searching these queries. The framework overcomes previous limitations by learning both user and item embeddings in the language space. To well-capture user interests with different aspects and granularity for improving relevance and diversity, we propose a multi-query generation technique with beam search. The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items. With GPT-2 language model and BM25 search engine, our framework outperforms state-of-the-art methods by $75.7\%$ and $22.2\%$ in Recall@K on two public datasets. Experiments further revealed that multi-query generation with beam search improves both the diversity of retrieved items and the coverage of a user's multi-interests. The adaptiveness and interpretability of generated queries are discussed with qualitative case studies.

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Cited by 15 Pith papers

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

  1. LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains

    cs.IR 2026-03 unverdicted novelty 7.0 of 10

    LLMAR applies LLM reasoning with a self-correction reflection loop to generate semantic user motives for tuning-free recommendations, showing up to 54.6% nDCG@10 gains on a sparse industrial dataset over trained baselines.

  2. Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding

    cs.AI 2026-07 conditional novelty 6.0 of 10

    GenCDSR combines shared/domain-specific item tokenization with serial-parallel decoding, improving cross-domain sequential recommendation accuracy by ~1.5% while cutting inference latency by ~85%.

  3. RecRec: Recursive Refinement for Sequential Recommendation

    cs.IR 2026-07 conditional novelty 6.0 of 10

    Recursive latent-state refinement with an evidence-anchored correction gate matches or beats much larger sequential, graph, and LLM recommenders on three benchmark datasets.

  4. Understanding Generative Recommendation with Semantic IDs from a Model-scaling View

    cs.AI 2025-09 conditional novelty 6.0 of 10

    Semantic-ID-based generative recommenders saturate as model size grows, while directly using an LLM as the recommender keeps improving with scale and learns collaborative filtering signals better.

  5. Efficient Item ID Generation for Large-Scale LLM-based Recommendation

    cs.IR 2025-09 conditional novelty 6.0 of 10

    LLM-based recommenders can treat item IDs as single direct embeddings and decode in one step, with a two-level softmax for efficiency and quality matching or beating multi-token models.

  6. Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders

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

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  8. Generative Multi-Target Cross-Domain Recommendation

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    GMC uses shared discrete semantic item IDs and a unified generative recommender with domain-specific LoRA to improve multi-target cross-domain recommendation.

  9. CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems

    cs.IR 2025-06 conditional novelty 6.0 of 10

    CoVE assigns each item a unique token ID, tunes item embeddings and the LM head, and predicts the next item from logits, beating finetune-and-retrieval baselines by up to 62 percent with a 16x compressed embedding table.

  10. Architecture is All You Need: Improving LLM Recommenders by Dropping the Text

    cs.IR 2025-06 conditional novelty 6.0 of 10

    A 2.2M-parameter Llama-style model using only item IDs beats a 7B-parameter text-based LLM recommender and SASRec on MovieLens-10M.

  11. SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management

    cs.IR 2025-10 conditional novelty 5.0 of 10

    A deployed LLM risk-investigation system that augments retrieval and reflection with a domain knowledge base achieves 0.92 factual alignment and 82% expert acceptance at JD.com.

  12. RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations

    cs.LG 2025-09 conditional novelty 5.0 of 10

    RecMind aligns LLM text embeddings with LightGCN collaborative embeddings via contrastive learning and a learned gate, achieving the best reported scores on all 8 ranking metrics across two datasets.

  13. RecLLM-R1: A Two-Stage Training Paradigm with Reinforcement Learning and Chain-of-Thought v1

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A two-stage SFT + GRPO training recipe with chain-of-thought reasoning improves LLM-based sequential recommendation on Amazon and industrial data, but the reported evaluation does not support the diversity and novelty claims.

  14. 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.

  15. Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model

    cs.IR 2025-07 conditional novelty 2.0 of 10

    Replacing Llama2 with Llama3.1 in the LlamaRec framework improved recommendation metrics by 8 to 39 percent on three public datasets.

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