mlsanityData to 14 Oct 2026

Recent Developments in Recommender Systems: A Survey

arXiv 2306.12680 ↗First posted 22 Jun 2023cs.IR cs.AI cs.LGcited by 14

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Abstract (arXiv)

In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems, and then delves into the category of knowledge-based recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, the study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 89 of its 229 references that are in the map. First posted on arXiv 22 Jun 2023.

Builds on

89 of 229 references.

References · 89

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
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38 more
  1. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  2. 2020Self-supervised Graph Learning for Recommendationcited by 305
  3. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  4. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 240
  5. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
  6. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  7. 2018DKN: Deep Knowledge-Aware Network for News Recommendationcited by 137
  8. 2017Deep Learning based Recommender System: A Survey and New Perspectivescited by 136
  9. 2019MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendationcited by 121
  10. 2020Disentangled Graph Collaborative Filteringcited by 118
  11. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 113
  12. 2020Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender Systemcited by 94
  13. 2021Learning Intents behind Interactions with Knowledge Graph for Recommendationcited by 94
  14. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  15. 2017Joint Deep Modeling of Users and Items Using Reviews for Recommendationcited by 84
  16. 2019NPA: Neural News Recommendation with Personalized Attentioncited by 73
  17. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  18. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
  19. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  20. 2021Fairness in Rankings and Recommendations: An Overviewcited by 62
  21. 2014word2vec Parameter Learning Explainedcited by 53
  22. 2018Adversarial Personalized Ranking for Recommendationcited by 49
  23. 2021Knowledge-aware Coupled Graph Neural Network for Social Recommendationcited by 49
  24. 2021Data Poisoning Attacks to Deep Learning Based Recommender Systemscited by 43
  25. 2019Jointly Learning Explainable Rules for Recommendation with Knowledge Graphcited by 40
  26. 2020MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendationcited by 39
  27. 2021Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendationcited by 39
  28. 2020Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systemscited by 38
  29. 2021Black-Box Attacks on Sequential Recommenders via Data-Free Model Extractioncited by 37
  30. 2021PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotioncited by 37
  31. 2018Estimating Position Bias without Intrusive Interventionscited by 32
  32. 2020Revisiting Adversarially Learned Injection Attacks Against Recommender Systemscited by 32
  33. 2021Double-Scale Self-Supervised Hypergraph Learning for Group Recommendationcited by 32
  34. 2018Adversarial Training Towards Robust Multimedia Recommender Systemcited by 28
  35. 2019Sequential Scenario-Specific Meta Learner for Online Recommendationcited by 28
  36. 2022Dbias: Detecting biases and ensuring Fairness in news articlescited by 28
  37. 2020Multi-Graph Convolution Collaborative Filteringcited by 27
  38. 2015Neural Network Matrix Factorizationcited by 26

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Citing papers per year: 2023: 1; 2024: 5; 2025: 3; 2026: 5.

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  1. 2025A Survey on Personalized and Pluralistic Preference Alignment in Large Language Modelscited by 27
  2. 2024ContextGNN: Beyond Two-Tower Recommendation Systemscited by 19
  3. 2024Let's Get It Started: Fostering the Discoverability of New Releases on Deezercited by 5
  4. 2024CSRec: Rethinking Sequential Recommendation from A Causal Perspectivecited by 4
  5. 2024Bipartite Graph Variational Auto-Encoder with Fair Latent Representation to Account for Sampling Bias in Ecological Networkscited by 1
  6. 2025RSAttAE: An Information-Aware Attention-based Autoencoder Recommender Systemcited by 1
  7. 2023Hypergraph-Enhanced Dual Convolutional Network for Bundle Recommendationcited by 0
  8. 2024A Survey of Latent Factor Models in Recommender Systemscited by 0
  9. 2025UniMLR: Modeling Implicit Class Significance for Multi-Label Rankingcited by 0
  10. 2026Socially-Aware Recommender Systems Mitigate Opinion Clusterizationcited by 0
  11. 2026Learning Robust Recommenders from Noisy Implicit Feedback via GMM-Weighted Bayesian Transition Matrixcited by 0
  12. 2026Rank-Constrained Deep Matrix Completion for Group Recommendationcited by 0
2 more
  1. 2026Temporal Preference Optimization for Unsupervised Retrievalcited by 0
  2. 2026Privacy in Personalized AI Is a System Property, Not Just a Model Propertycited by 0