Pith. sign in

REVIEW 2 cited by

Moment&Cross: Next-Generation Real-Time Cross-Domain CTR Prediction for Live-Streaming Recommendation at Kuaishou

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2408.05709 v1 pith:HCTMNTTH submitted 2024-08-11 cs.IR

Jiangxia Cao , Shen Wang , Yue Li , Shenghui Wang , Jian Tang , Shiyao Wang , Shuang Yang , Zhaojie Liu
show 1 more author
Guorui Zhou
This is my paper
classification cs.IR
keywords live-streamingshort-videorecommendationcontentuserusersbehaviorschallenging
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Kuaishou, is one of the largest short-video and live-streaming platform, compared with short-video recommendations, live-streaming recommendation is more complex because of: (1) temporarily-alive to distribution, (2) user may watch for a long time with feedback delay, (3) content is unpredictable and changes over time. Actually, even if a user is interested in the live-streaming author, it still may be an negative watching (e.g., short-view < 3s) since the real-time content is not attractive enough. Therefore, for live-streaming recommendation, there exists a challenging task: how do we recommend the live-streaming at right moment for users? Additionally, our platform's major exposure content is short short-video, and the amount of exposed short-video is 9x more than exposed live-streaming. Thus users will leave more behaviors on short-videos, which leads to a serious data imbalance problem making the live-streaming data could not fully reflect user interests. In such case, there raises another challenging task: how do we utilize users' short-video behaviors to make live-streaming recommendation better?

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reward Guided Decoding for Generative Recommendation

    cs.IR 2026-07 conditional novelty 5.0 of 10

    Reward-guided decoding reweights generative recommender probabilities by a learned business-value score, log P + R/β, and is deployed at Kuaishou.

  2. Towards Generalizable Safety in Crowd Navigation via Conformal Uncertainty Handling

    cs.RO 2025-08 unverdicted novelty 5.0 of 10

    A crowd navigation method augmenting reinforcement learning with conformal uncertainty estimates is claimed to cut collisions under distribution shift, but the manuscript body is an unrelated live streaming dataset paper.

Pith tools