https://github.com/amitkumarj441/wsdm_recsys
Notes on the latest work from WSDM 2024/2025 in the area of recommender systems
https://github.com/amitkumarj441/wsdm_recsys
Last synced: 3 months ago
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Notes on the latest work from WSDM 2024/2025 in the area of recommender systems
- Host: GitHub
- URL: https://github.com/amitkumarj441/wsdm_recsys
- Owner: amitkumarj441
- License: gpl-3.0
- Created: 2023-12-25T20:45:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-01T07:20:12.000Z (6 months ago)
- Last Synced: 2025-01-26T14:11:59.417Z (5 months ago)
- Homepage:
- Size: 42 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Paper highlights from WSDM 2024 on Recommendation Systems
Notes on the latest work from [WSDM 2024](https://www.wsdm-conference.org/2024) in the area of recommender systems
➡️ __Only include details of papers that have preprints available online.__
1. [Defense Against Model Extraction Attacks on Recommender Systems](https://arxiv.org/pdf/2310.16335.pdf)
Primer:
2. [Motif-based Prompt Learning for Universal Cross-domain Recommendation](https://arxiv.org/pdf/2310.13303.pdf)
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3. [To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders](https://arxiv.org/pdf/2310.14079.pdf)
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4. [Linear Recurrent Units for Sequential Recommendation](https://arxiv.org/pdf/2310.02367.pdf)
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5. [User Behavior Enriched Temporal Knowledge Graph for Sequential Recommendation]([https://holdenhu.github.io/publications/](https://www.comp.nus.edu.sg/~kanmy/papers/TKGSRec__WSDM__CameraReady_17_Dec__8_2_pages_.pdf))
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6. [CausalMMM: Learning Causal Structure for Marketing Mix Modeling]()
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7. [Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation](https://arxiv.org/pdf/2310.14318)
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8. [Budgeted Embedding Table For Recommender Systems](https://arxiv.org/pdf/2310.14884)
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9. [Pre-trained Recommender Systems: A Causal Debiasing Perspective](https://arxiv.org/pdf/2310.19251)
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10. [Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation](https://arxiv.org/pdf/2402.02855)
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11. [PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation](https://arxiv.org/pdf/2312.01916.pdf)
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12. [Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation](https://arxiv.org/pdf/2311.01056)
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13. [Unified Pretraining for Recommendation via Task Hypergraphs](https://arxiv.org/pdf/2310.13286)
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14. [SSLRec: A Self-Supervised Learning Library for Recommendation](https://arxiv.org/pdf/2308.05697)
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15. [Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation]()
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16. [LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting](https://arxiv.org/pdf/2312.10947)
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17. [MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation](https://arxiv.org/pdf/2312.09511)
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18. [Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure](https://arxiv.org/pdf/2312.07036)
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19. [Knowledge Graph Diffusion Model for Recommendation](https://github.com/HKUDS/DiffKG)
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20. [Large Language Models for Data Aumgnetation in Recommendation](https://arxiv.org/pdf/2311.00423.pdf) - Current preprint title is __LLMRec: Large Language Models with Graph Augmentation for Recommendation__
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21. [Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction](https://arxiv.org/pdf/2310.18770)
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22. [Interact with the Explanations: Causal Debiased Explainable Recommendation System](https://shuaili8.github.io/publications.html)
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23. [Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation]()
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24. [MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems](https://dgliu.github.io)
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25. [MADM: A Model-agnostic Denoising Module for Graph-based Social Recommendation]()
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26. [CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process](https://arxiv.org/pdf/2401.12732)
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27. [Inverse Learning with Extremely Sparse Feedback for Recommendation](https://arxiv.org/pdf/2311.08302)
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28. [Contextual MAB Oriented Embedding Denoising for Sequential Recommendation](https://www.lichenliang.net)
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29. [Mixed Attention Network for Cross-domain Sequential Recommendation](https://arxiv.org/pdf/2311.08272)
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30. [Knowledge Graph Context-Enhanced Diversified Recommendation](https://arxiv.org/pdf/2310.13253)
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31. [Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights](https://arxiv.org/pdf/2305.15036)
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32. [Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation](https://wyhwhy.github.io)
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33. [AutoPooling: Automated Pooling Search for Multi-valued Features in Recommendations]()
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34. [C^2DR: Robust Cross-Domain Recommendation based on Causal Disentanglement]()
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35. [RecJPQ: Training Large-Catalogue Sequential Recommenders](https://arxiv.org/pdf/2312.06165)
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36. [On the Effectiveness of Unlearning in Session-Based Recommendation](https://arxiv.org/pdf/2312.14447.pdf)
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37. [Proxy-based Item Representation for Attribute and Context-aware Recommendation](https://arxiv.org/pdf/2312.06145)
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38. [IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation]()
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39. [Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation](https://arxiv.org/abs/2401.07769)
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40. [User Consented Federated Recommender System Against Personalized Attribute Inference Attack](https://arxiv.org/pdf/2312.16203)
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41. [Neural Kalman Filtering for Robust Temporal Recommendation]()
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42. [ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://arxiv.org/abs/2305.06566)
Primer: Addressing the limitations of existing content-based recommender systems, this paper presents the ONCE framework, which leverages both open- and closed-source large language models (LLMs) to significantly enhance recommendation performance. Their findings demonstrate that combining finetuning on open-source LLMs with prompting-based data augmentation on closed-source models yields substantial improvements, with relative gains reaching up to 19.32% compared to state-of-the-art models. These results highlight the immense potential of LLMs in content-based recommendation and hold significant implications for online content platforms. Notably, the ONCE framework extends beyond news and book recommendation, demonstrating its applicability to diverse domains.
## Paper highlights from WSDM 2025 on Recommendation Systems
1. [Review-Based Hyperbolic Cross-Domain Recommendation](https://arxiv.org/pdf/2403.20298) - Old version of this paper on Arxiv
2. [Large Language Model driven Policy Exploration for Recommender Systems](https://eprints.gla.ac.uk/340195/)
3. [Combating Heterogeneous Model Biases in Recommendations via Boosting]()
4. [Teach Me How to Denoise: a Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration]()