Beyond Relevance: Improving User Engagement by Personalization for Short-Video Search
Wentian Bao, Hu Liu, Kai Zheng, Chao Zhang, Shunyu Zhang, Enyun Yu,, Wenwu Ou, Yang Song

TL;DR
This paper introduces $ ext{PR}^2$, a comprehensive personalization system for short-video search that significantly enhances user engagement by leveraging collaborative filtering, dense retrieval, and user preference modeling.
Contribution
The paper presents $ ext{PR}^2$, a novel personalized search framework combining multiple techniques, and demonstrates its effectiveness through deployment in a real-world short-video platform.
Findings
10.2% increase in CTR@10
20% increase in video watch time
1.6% uplift in search DAU
Abstract
Personalized search has been extensively studied in various applications, including web search, e-commerce, social networks, etc. With the soaring popularity of short-video platforms, exemplified by TikTok and Kuaishou, the question arises: can personalization elevate the realm of short-video search, and if so, which techniques hold the key? In this work, we introduce , a novel and comprehensive solution for personalizing short-video search, where stands for the Personalized Retrieval and Ranking augmented search system. Specifically, leverages query-relevant collaborative filtering and personalized dense retrieval to extract relevant and individually tailored content from a large-scale video corpus. Furthermore, it utilizes the QIN (Query-Dominate User Interest Network) ranking model, to effectively harness user long-term preferences and…
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Taxonomy
TopicsRecommender Systems and Techniques · Video Analysis and Summarization
