Balancing Semantic Relevance and Engagement in Related Video Recommendations

• URL: http://arxiv.org/abs/2507.09403v1
• Date: Sat, 12 Jul 2025 21:04:25 GMT
• Title: Balancing Semantic Relevance and Engagement in Related Video Recommendations
• Authors: Amit Jaspal, Feng Zhang, Wei Chang, Sumit Kumar, Yubo Wang, Roni Mittleman, Qifan Wang, Weize Mao,
• Abstract summary: Related video recommendations commonly use collaborative filtering (CF) driven by co-engagement signals.<n>This paper introduces a novel multi-objective retrieval framework to balance semantic relevance and user engagement.
• Score: 21.2575040646784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Related video recommendations commonly use collaborative filtering (CF) driven by co-engagement signals, often resulting in recommendations lacking semantic coherence and exhibiting strong popularity bias. This paper introduces a novel multi-objective retrieval framework, enhancing standard two-tower models to explicitly balance semantic relevance and user engagement. Our approach uniquely combines: (a) multi-task learning (MTL) to jointly optimize co-engagement and semantic relevance, explicitly prioritizing topical coherence; (b) fusion of multimodal content features (textual and visual embeddings) for richer semantic understanding; and (c) off-policy correction (OPC) via inverse propensity weighting to effectively mitigate popularity bias. Evaluation on industrial-scale data and a two-week live A/B test reveals our framework's efficacy. We observed significant improvements in semantic relevance (from 51% to 63% topic match rate), a reduction in popular item distribution (-13.8% popular video recommendations), and a +0.04% improvement in our topline user engagement metric. Our method successfully achieves better semantic coherence, balanced engagement, and practical scalability for real-world deployment.

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