arXiv：cs.AI· Chi Liu, Jiangxia Cao, Rui Huang, Kuo Cai, Weifeng Ding, Qiang Luo, Kun Gai, Guorui Zhou·· 13 小时前
快手提出 CRM：用可控条件检索模型提升短视频观看时长
CRM: Retrieval Model with Controllable Condition
AI 导读
快手提出 Controllable Retrieval Model（CRM），一种将观看时长作为用户塔输入的结果条件化检索框架，训练时学习根据观测观看时长预测下一物品，推理时直接输入目标观看时长即可调控检索，无需重训。
正文
Abstract:Retrieval models for recommendation need to account for outcomes such as watch time while searching large item corpora efficiently. We present the Controllable Retrieval Model (CRM), an outcome-conditioned retrieval framework that adds watch time as an input to the user tower. During training, CRM learns to predict the next item given its observed watch time; at inference, we supply a desired watch time to steer retrieval without retraining. The item tower is independent of the condition, so item embeddings can still be precomputed and indexed for approximate nearest neighbor (ANN) search. We implement CRM as both a DNN-based two-tower model and a Decision Transformer. The Transformer interleaves item tokens with watch-time-to-go condition tokens, using the target watch time as a numerical prompt for next-item prediction. For each online request, we choose between the average and maximum watch times in the user's recent history as the condition. CRM is deployed in Kuaishou's short-video recommendation system, which serves over 400 million users. Online A/B tests show video watch time improvements of 0.372% in Kuaishou and 0.457% in Kuaishou Lite.
| Comments: | 4 pages. Revised version with improved presentation |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2412.13844 [cs.IR] |
| (or arXiv:2412.13844v2 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2412.13844 arXiv-issued DOI via DataCite |
Submission history
From: Chi Liu [view email]
[v1]
Wed, 18 Dec 2024 13:37:36 UTC (584 KB)
[v2]
Fri, 9 Oct 2026 08:54:30 UTC (581 KB)
来源：arXiv：cs.AI · arxiv.org