Kdd 2025 Controlling Diversity At Inference Guiding Diffusion Recommender Models

Visa Information Kdd 2025 The desired degree of diversity may fluctuate based on users' daily moods or business strategies. however, existing methods for controlling diversity often lack flexibility, as diversity is decided during training and cannot be easily modified during inference. Con trolling diversity at inference: guiding difusion recommender models with targeted category preferences. in proceedings of the 31th acm sigkdd conference on knowledge discovery and data mining (kdd ’25).

A Survey On Diffusion Models For Recommender Systems Ai Research Kdd 2025 controlling diversity at inference: guiding diffusion recommender models association for computing machinery (acm) 45.1k subscribers subscribed. Interested users can choose to read all 250 kdd 2025 papers (volume 1) in our digest console. to search for papers presented at kdd 2025 on a specific topic, please make use of the search by venue (kdd 2025) service. Diversity control is an important task to alleviate bias amplification and filter bubble problems. the desired degree of diversity may fluctuate based on users' daily moods or business. I am a postdoctoral researcher at the university of illinois urbana champaign (uiuc). i received my ph.d. from postech, advised by prof. hwanjo yu, and subsequently worked as a postdoctoral researcher at postech institute of artificial intelligence. my research interests include information retrieval, large language models, and trustworthy ai.

Kdd 2025 Piers Clark Diversity control is an important task to alleviate bias amplification and filter bubble problems. the desired degree of diversity may fluctuate based on users' daily moods or business. I am a postdoctoral researcher at the university of illinois urbana champaign (uiuc). i received my ph.d. from postech, advised by prof. hwanjo yu, and subsequently worked as a postdoctoral researcher at postech institute of artificial intelligence. my research interests include information retrieval, large language models, and trustworthy ai. Proceedings of the 31st acm sigkdd conference on knowledge discovery and data mining, v.1, kdd 2025, toronto, on, canada, august 3 7, 2025. acm 2025, isbn 979 8 4007 1245 6. [kdd'25] controlling diversity at inference: guiding diffusion recommender models with targeted category preferences c0natus d3rec. [kdd'25] controlling diversity at inference: guiding diffusion recommender models with targeted category preferences. Proceedings of the 31st acm sigkdd conference on knowledge discovery and data mining, v.1, kdd 2025, toronto, on, canada, august 3 7, 2025.