SSRLive: Live Streaming Recommendation with Dynamic Semantic ID

• URL: http://arxiv.org/abs/2606.06970v1
• Date: Fri, 05 Jun 2026 06:58:07 GMT
• Title: SSRLive: Live Streaming Recommendation with Dynamic Semantic ID
• Abstract summary: We introduce SSRLive: Dynamic Semantic ID-guided Streaming Recommendation for Live platforms.<n>The proposed framework integrates a generative module and a discriminative module in a unified architecture.<n>The discriminative component refines task-specific representations by combining SIDs with user features, augments them with user-streamer interaction data, and performs multi-task predictions.
• Score: 8.32878203748523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Live streaming has emerged as one of the fastest-growing forms of online media, enabling instant content broadcasting and real-time engagement between users and streamers. Despite the effectiveness of existing recommendation algorithms in this domain, they often suffer from limited utilization of computational resources, with low FLOPs that hinder further performance enhancement. Generative recommendation techniques, which have gained traction in various industrial tasks, offer a promising avenue for improving live streaming recommendations. However, directly applying generative methods to live streaming is non-trivial due to two major challenges: (1) static semantic IDs (SIDs) cannot reflect the rapidly changing nature of live room content; and (2) generative pipelines generally do not incorporate user--streamer interaction signals (e.g., likes, orders), which are critical for modeling user intent toward both the streamer and showcased products. To address these challenges, we introduce SSRLive: Dynamic Semantic ID-guided Streaming Recommendation for Live platforms. The proposed framework integrates a generative module and a discriminative module in a unified architecture. The generative component employs an encoder-decoder design to produce both static and dynamic SIDs, enabling timely representation of live room content while leveraging multimodal information. The discriminative component refines task-specific representations by combining SIDs with user features, augments them with user-streamer interaction data, and performs multi-task predictions. Online A/B tests in real-world deployment demonstrate tangible benefits: watch time (+3.38%), GMV (+0.72%), follower growth (+3.12%), and interaction volume (+2.92%). These improvements highlight the effectiveness and business value of SSRLive, which is now fully deployed, serving hundreds of millions of active users.

Related papers

• TAGR: Temporally Adaptive Generative Recommendation for Industrial Live-Streaming Advertising [41.7689835433814]
  We propose a generative recommendation framework with temporal adaptation at three levels: live-ad tokenization, user intent modeling, and preference alignment.<n> deployed on a large-scale e-commerce live-stream advertising platform, TAGR improves live-room entry and shopping-cart click rates by 8.5% and 7.4%, respectively.
  arXiv  Detail & Related papers  (2026-08-25T03:47:52Z)
• Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting [1.1259214300411828]
  One of the most challenging problems entertainment live-streaming services face is that user behaviors are sparse and delayed.<n>We propose a delayed window approach that extends feedback collection beyond immediate responses.<n>We also propose a multi-model architecture that combines fresh and delayed signals, and a segment-aware targeting module.
  arXiv  Detail & Related papers  (2026-08-05T05:14:39Z)
• Harnessing Streaming Video in the Wild [53.23721420272668]
  Vision-Language Models (VLMs) are increasingly required to process video streams in applications such as video-call assistants, live commentary, and embodied robots.<n>An ideal streaming system should support proactive interaction, long-horizon memory, and real-time processing.<n>ExistingVLMs excel at offline video understanding but fall short in streaming capabilities and lack dedicated infrastructure for streaming deployment.
  arXiv  Detail & Related papers  (2026-06-07T13:00:19Z)
• Bridging Short Videos and Live Streams: Reasoning-Guided Multimodal LLMs for Cross-Domain Representation Learning [38.08336224801579]
  Reasoning-Guided Cross-Domain Representation Learning (RGCD-Rep)<n>RGCD-Rep is a reasoning-guided framework for cross-domain recommendation from short videos to live streams.<n>It is fully deployed and serves over 400 million users daily.
  arXiv  Detail & Related papers  (2026-06-03T04:49:01Z)
• Advancing Narrative Long Video Generation via Training-Free Identity-Aware Memory [79.01059178883817]
  IAMFlow is a training-free identity-aware memory framework that explicitly models and tracks persistent entity identities.<n>A VLM asynchronously verifies and refines attributes from rendered frames, enabling explicit entity tracking in place of implicit similarity-based matching.<n>NarraStream-Bench is a benchmark for narrative streaming video generation that features 324 multi-prompt scripts spanning six dimensions.
  arXiv  Detail & Related papers  (2026-05-18T17:54:34Z)
• Action-Aware Generative Sequence Modeling for Short Video Recommendation [50.74467504063892]
  Short videos often contain diverse segments, and users may not hold the same attitude toward all of them.<n>Traditional binary-classification recommendation models, which treat a video as a single holistic entity, face limitations in accurately capturing such nuanced preferences.<n>We propose a novel modeling paradigm: Action-Aware Generative Sequence Network (A2Gen), which refines user actions along the temporal dimension.
  arXiv  Detail & Related papers  (2026-04-28T16:41:04Z)
• OneLive: Dynamically Unified Generative Framework for Live-Streaming Recommendation [49.95897358060393]
  We propose OneLive, a dynamically unified generative recommendation framework tailored for live-streaming scenario.<n>OneLive integrates four key components: (i) A Dynamic Tokenizer that continuously encodes evolving real-time live content fused with behavior signal through residual quantization; (ii) A Time-Aware Gated Attention mechanism that explicitly models temporal dynamics for timely decision making; (iii) An efficient decoder-only generative architecture enhanced with Sequential MTP and QK Norm for stable training and accelerated inference.
  arXiv  Detail & Related papers  (2026-02-09T12:56:39Z)
• StreamDiffusionV2: A Streaming System for Dynamic and Interactive Video Generation [65.90400162290057]
  Generative models are reshaping the live-streaming industry by redefining how content is created, styled, and delivered.<n>Recent advances in video diffusion have markedly improved temporal consistency and sampling efficiency for offline generation.<n>Live online streaming operates under strict service-level objectives (SLOs): time-to-first-frame must be minimal, and every frame must meet a per-frame deadline with low jitter.
  arXiv  Detail & Related papers  (2025-11-10T18:51:28Z)
• Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts [38.55001831149498]
  multimodal streaming recommender systems are widely deployed in real-world applications, where user interests shift over time.<n>We propose Expandable Side Mixture-of-Experts (XSMoE), a memory-efficient framework for multimodal streaming recommendation.<n>XSMoE attaches lightweight side-tuning modules to frozen pretrained encoders and incrementally expands them in response to evolving user feedback.
  arXiv  Detail & Related papers  (2025-08-08T04:00:05Z)
• Iris: Breaking GUI Complexity with Adaptive Focus and Self-Refining [67.87810796668981]
  Information-Sensitive Cropping (ISC) and Self-Refining Dual Learning (SRDL)<n>Iris achieves state-of-the-art performance across multiple benchmarks with only 850K GUI annotations.<n>These improvements translate to significant gains in both web and OS agent downstream tasks.
  arXiv  Detail & Related papers  (2024-12-13T18:40:10Z)
• Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition [45.0131792009999]
  We propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. Our network outperforms state-of-the-art approaches in most standard evaluation settings.
  arXiv  Detail & Related papers  (2023-07-22T03:51:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.