mlsanityData to 7 Oct 2026

Temporal Meta-path Guided Explainable Recommendation

arXiv 2101.01433 ↗First posted 5 Jan 2021cs.SIcited by 26

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Abstract (arXiv)

This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture user historical item features and path-based context to characterise next purchased item. Extensive evaluations of TMER on three real-world benchmark datasets show state-of-the-art performance compared against recent strong baselines.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 7; 2023: 7; 2024: 4; 2025: 0; 2026: 1.

26 papers.

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  1. 2023User Behavior Simulation with Large Language Model based Agentscited by 136
  2. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  3. 2021Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendationcited by 21
  4. 2024MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretationcited by 18
  5. 2022Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendationcited by 14
  6. 2021Recommender systems based on graph embedding techniques: A comprehensive reviewcited by 8
  7. 2021Comparative Explanations of Recommendationscited by 8
  8. 2022Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolutioncited by 8
  9. 2024Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approachcited by 6
  10. 2021Reinforcement Learning based Path Exploration for Sequential Explainable Recommendationcited by 4
  11. 2023Counterfactual Collaborative Reasoningcited by 3
  12. 2023Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filteringcited by 1
14 more
  1. 2023Do as I can, not as I getcited by 1
  2. 2023Group-Aware Interest Disentangled Dual-Training for Personalized Recommendationcited by 1
  3. 2024Review of Explainable Graph-Based Recommender Systemscited by 1
  4. 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysiscited by 0
  5. 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Searchcited by 0
  6. 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generationcited by 0
  7. 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectorscited by 0
  8. 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networkscited by 0
  9. 2022Explanation-Guided Fairness Testing through Genetic Algorithmcited by 0
  10. 2022Using Graph Neural Networks for Program Terminationcited by 0
  11. 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGramcited by 0
  12. 2023Variational Counterfactual Prediction under Runtime Domain Corruptioncited by 0
  13. 2024Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Surveycited by 0
  14. 2026Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Searchcited by 0