Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration | Paper Citation Record

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Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration GPT-4 Technical Report

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Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation

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Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration In International Conference on Learning Representations

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Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders

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Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration A Content-Driven Micro-Video Recommendation Dataset at Scale

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Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

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Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration BPR: Bayesian Personalized Ranking from Implicit Feedback

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-16T11:58:43.840370Z

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Observation ce5a97e8-9cad-49da-871b-2b360519e5d2 · outbound

This paper cites In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval.

Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-16T11:58:43.710308Z

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Observation 88f0b513-08d8-48ae-bad5-45792842a18a · outbound

This paper cites In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval.

Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:45.208559Z

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Observation aaddf217-f3a8-47ba-abb5-fe3d0e7dfd41 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T11:58:43.723449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f14209d9-86f7-4175-84e7-3bd9a70b2293 · outbound

This paper cites InProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration InProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:44.641728Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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