Diffusion-based recommenders learn user preferences by denoising interaction histories, but the added noise can distort the original preference signals. Flow-based collaborative filtering (CF) instead learns a discrete path from a sampled source to the user's observed training interactions (the target history), enablin...
Cheng-Hao Wu, Yong-Qi Zhou, Jin Chen et al.· 0 citations
Long-sequence modeling is increasingly important in recommender systems for capturing users’ evolving and long-term interests. In advertising, however, user interaction histories are often highly sparse due to limited exposure opportunities, making ad-only behavior sequences insufficient for effective long-sequence rec...
Xian Hu, Ming Yue, Zhi-Xiang Feng et al.· Proceedings of the 20th ACM...· 0 citations
ChronicleRec is a pre-train-and-transfer framework that compresses an ultra-long behavior sequence once into a chronologically ordered set of Chronicle Tokens, which can be cached per user, decoupling ultra-long sequence modeling from online candidate scoring.
Chengkai Huang, Yu-Bin Sheng, Liang Guo et al.· 0 citations
UniTraj, a practical framework that extends sequence construction beyond the advertising domain by incorporating behaviors from content-consumption scenarios, forming unified commercial trajectories across domains and scenarios, is proposed and deployed in a large-scale online advertising system.
Xian Hu, Ming Yue, Zhi-Xiang Feng et al.· Proceedings of the 20th ACM...· 0 citations
DLMRec is proposed, a discrete diffusion language model tailored for recommendation that offers a compelling alternative to autoregressive generation and introduces three key components to bridge diffusion language modeling with recommendation.
Chengyi Liu, Yong-Qi Zhou, Jun-Wei Pan et al.· arXiv.org· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
