Abstract:Modern recommender systems rely heavily on ID-based collaborative filtering: each item is represented by a unique ID embedding that accumulates collaborative signals from user interactions. Livestreaming recommendation, however, faces a unique challenge in this paradigm: a live room typically broadcasts for only tens of minutes, so its item ID remains poorly learned in a persistent cold-start state and ID-centric ranking models fail to generalize. We present FLUID, the first framework to fully retire the candidate-side item ID from a production-scale livestreaming ranker. FLUID couples a cross-domain multimodal encoder, jointly trained on short videos and livestreams to produce discrete hierarchical codes (LUCID), with a late-fusion, ID-free design that injects slice-level and room-level LUCID as independent tokens, stabilized by a staged warmup under online incremental training. Deployed on our industrial livestreaming recommenders with a cross-platform combined user base of over one billion globally, FLUID delivers significant online gains of +0.55% Quality Watch Duration, +2.05% Cold-Start Room Views, and +0.05% Active Hours.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.21832 [cs.AI] |
| (or arXiv:2605.21832v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21832 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zexi Huang [view email]
[v1]
Wed, 20 May 2026 23:52:51 UTC (19,066 KB)