mlsanityData to 7 Oct 2026

Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

arXiv 2202.06466 ↗First posted 14 Feb 2022cs.IR cs.AIcited by 14

Not on the map. Why?

Abstract (arXiv)

Explainable recommendation has shown its great advantages for improving recommendation persuasiveness, user satisfaction, system transparency, among others. A fundamental problem of explainable recommendation is how to evaluate the explanations. In the past few years, various evaluation strategies have been proposed. However, they are scattered in different papers, and there lacks a systematic and detailed comparison between them. To bridge this gap, in this paper, we comprehensively review the previous work, and provide different taxonomies for them according to the evaluation perspectives and evaluation methods. Beyond summarizing the previous work, we also analyze the (dis)advantages of existing evaluation methods and provide a series of guidelines on how to select them. The contents of this survey are based on more than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys, UMAP, and IUI, and their complete summarization are presented at https://shimo.im/sheets/VKrpYTcwVH6KXgdy/MODOC/. With this survey, we finally aim to provide a clear and comprehensive review on the evaluation of explainable recommendation.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 17 of its 68 references that are in the map. First posted on arXiv 14 Feb 2022.

Builds on

17 of 68 references.

References · 17

  1. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  2. 2020Fairness-Aware Explainable Recommendation over Knowledge Graphscited by 74
  3. 2021Counterfactual Explainable Recommendationcited by 60
  4. 2021Personalized Transformer for Explainable Recommendationcited by 58
  5. 2018Explainable Recommendation via Multi-Task Learning in Opinionated Text Datacited by 40
  6. 2020Try This Instead: Personalized and Interpretable Substitute Recommendationcited by 34
  7. 2019The FacT: Taming Latent Factor Models for Explainability with Factorization Treescited by 27
  8. 2020Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descentcited by 26
  9. 2020CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendationcited by 26
  10. 2021Temporal Meta-path Guided Explainable Recommendationcited by 26
  11. 2018Explainable Reasoning over Knowledge Graphs for Recommendationcited by 18
  12. 2020Explainable Recommendations via Attentive Multi-Persona Collaborative Filteringcited by 15
5 more
  1. 2018Explaining Latent Factor Models for Recommendation with Influence Functionscited by 14
  2. 2016A Survey of Point-of-interest Recommendation in Location-based Social Networkscited by 11
  3. 2020Explainable Recommender Systems via Resolving Learning Representationscited by 11
  4. 2021Fairness and Transparency in Recommendation: The Users' Perspectivecited by 10
  5. 2021Attribute-aware Explainable Complementary Clothing Recommendationcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 5; 2024: 6; 2025: 1.

14 papers.

Sort:

By area:

  1. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  2. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  3. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  4. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendationcited by 26
  5. 2024Large Language Models as Evaluators for Recommendation Explanationscited by 19
  6. 2023Towards Explainable Conversational Recommender Systemscited by 4
  7. 2023Counterfactual Collaborative Reasoningcited by 3
  8. 2024Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendationcited by 3
  9. 2023GPT as a Baseline for Recommendation Explanation Textscited by 2
  10. 2024Review of Explainable Graph-Based Recommender Systemscited by 1
  11. 2023UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filteringcited by 0
  12. 2024What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenderscited by 0
2 more
  1. 2024Assistive AI for Augmenting Human Decision-makingcited by 0
  2. 2024Performance-Driven QUBO for Recommender Systems on Quantum Annealerscited by 0