FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation

2025-02-13

AI Summary

Live-stream recommendation faces two key challenges: (1) extreme sparsity of high-value user behaviors (e.g., likes, virtual gifting), and (2) severely limited exposure of live-stream content—only ~1/9 that of short videos—hindering effective personalized modeling. To address these, we propose a dual-domain collaborative modeling framework. First, we design an intra-domain frequency-aware module based on Discrete Fourier Transform (DFT) to explicitly capture discriminative temporal patterns from sparse high-value interactions. Second, we introduce an “alignment-first, then fusion” cross-domain paradigm: contrastive learning aligns behavioral representations between short-video and live-stream domains, while a customized attention mechanism enables robust preference transfer. Extensive offline experiments and online A/B tests on Kwai demonstrate significant improvements in CTR (+4.2%) and GMV (+3.8%). The method has been fully deployed in production, serving hundreds of millions of users.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Live-streaming services have attracted widespread popularity due to their real-time interactivity and entertainment value. Users can engage with live-streaming authors by participating in live chats, posting likes, or sending virtual gifts to convey their preferences and support. However, the live-streaming services faces serious data-sparsity problem, which can be attributed to the following two points: (1) User's valuable behaviors are usually sparse, e.g., like, comment and gift, which are easily overlooked by the model, making it difficult to describe user's personalized preference. (2) The main exposure content on our platform is short-video, which is 9 times higher than the exposed live-streaming, leading to the inability of live-streaming content to fully model user preference. To this end, we propose a Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation, termed as FARM. Specifically, we first present the intra-domain frequency aware module to enable our model to perceive user's sparse yet valuable behaviors, i.e., high-frequency information, supported by the Discrete Fourier Transform (DFT). To transfer user preference across the short-video and live-streaming domains, we propose a novel preference align before fuse strategy, which consists of two parts: the cross-domain preference align module to align user preference in both domains with contrastive learning, and the cross-domain preference fuse module to further fuse user preference in both domains using a serious of tailor-designed attention mechanisms. Extensive offline experiments and online A/B testing on Kuaishou live-streaming services demonstrate the effectiveness and superiority of FARM. Our FARM has been deployed in online live-streaming services and currently serves hundreds of millions of users on Kuaishou.

Problem

• Addresses data sparsity in live-streaming recommendations.
• Models user preferences across short-video and live-streaming domains.
• Enhances recommendation accuracy with frequency-aware techniques.

Innovation

• Frequency-aware module with DFT
• Cross-domain preference alignment
• Tailored attention mechanisms fusion