mlsanityData to 14 Oct 2026

Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training

arXiv 2305.07633 ↗First posted 12 May 2023cs.IR cs.AI cs.LGcited by 12

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Abstract (arXiv)

Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extract universal item representation via pre-trained language models (PLMs), they ignore the crucial item relationships. This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs. We identify three challenges for pre-training PKG, which are multi-type relations in PKG, semantic divergence between item generic information and relations and domain discrepancy from PKG to downstream ZSIR task. We address the challenges by proposing four pre-training tasks and novel task-oriented adaptation (ToA) layers. Moreover, this paper discusses how to fine-tune the model on new recommendation task such that the ToA layers are adapted to ZSIR task. Comprehensive experiments on 18 markets dataset are conducted to verify the effectiveness of the proposed model in both knowledge prediction and ZSIR task.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 12 May 2023.

Builds on

33 of 54 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 4; 2026: 1.

12 papers.

  1. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  2. 2023MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphscited by 40
  3. 2023Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendationcited by 13
  4. 2024Scaling Sequential Recommendation Models with Transformerscited by 7
  5. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 6
  6. 2025Towards Explainable Temporal User Profiling with LLMscited by 6
  7. 2024Fully Hyperbolic Rotation for Knowledge Graph Embeddingcited by 3
  8. 2025Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendationscited by 2
  9. 2024A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directionscited by 0
  10. 2025A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embeddingcited by 0
  11. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  12. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0