Pith. sign in

REVIEW 4 cited by

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 2504.05217 v1 pith:OQ6JNXRB submitted 2025-04-07 cs.IR

LLM-Alignment Live-Streaming Recommendation

Yueyang Liu , Jiangxia Cao , Shen Wang , Shuang Wen , Xiang Chen , Xiangyu Wu , Shuang Yang , Zhaojie Liu
show 2 more authors
Kun Gai Guorui Zhou
This is my paper
classification cs.IR
keywords live-streamingcontentdynamicreal-timerecommendationrecsysuseraccurately
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

In recent years, integrated short-video and live-streaming platforms have gained massive global adoption, offering dynamic content creation and consumption. Unlike pre-recorded short videos, live-streaming enables real-time interaction between authors and users, fostering deeper engagement. However, this dynamic nature introduces a critical challenge for recommendation systems (RecSys): the same live-streaming vastly different experiences depending on when a user watching. To optimize recommendations, a RecSys must accurately interpret the real-time semantics of live content and align them with user preferences.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

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

  1. KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation

    cs.IR 2025-08 accept novelty 8.0

    KuaiLive is the first publicly released real-time interactive dataset for live streaming recommendation, with logs from 23,772 users and 452,621 streamers over 21 days plus timestamps, multi-type interactions, and sid...

  2. FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation

    cs.AI 2026-05 unverdicted novelty 6.0

    FLUID introduces LUCID semantic codes from a multimodal encoder to retire item IDs in livestreaming rankers, with staged warmup yielding online gains of +0.55% watch duration and +2.05% cold-start views.

  3. SSRLive: Live Streaming Recommendation with Dynamic Semantic ID

    cs.IR 2026-06 unverdicted novelty 5.0

    SSRLive combines generative and discriminative modules with dynamic semantic IDs to improve live streaming recommendations, reporting gains of +3.38% watch time, +0.72% GMV, +3.12% follower growth, and +2.92% interact...

  4. FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation

    cs.AI 2026-05 unverdicted novelty 5.0

    FLUID retires candidate-side item IDs in production livestream rankers via cross-domain multimodal hierarchical codes and late-fusion ID-free design, reporting online gains of +0.55% Quality Watch Duration and +2.05% ...