Beyond Relevance: Improving User Engagement by Personalization for Short-Video Search

• Main Authors: Bao, Wentian, Liu, Hu, Zheng, Kai, Zhang, Chao, Zhang, Shunyu, Yu, Enyun, Ou, Wenwu, Song, Yang
• Format: Journal Article
• Language: English
• Published: 17-09-2024
• Subjects: Computer Science - Information Retrieval
• DOI: 10.48550/arxiv.2409.11281

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 $\text{PR}^2$, a novel and comprehensive solution
for personalizing short-video search, where $\text{PR}^2$ stands for the
Personalized Retrieval and Ranking augmented search system. Specifically,
$\text{PR}^2$ 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 real-time behaviors, and efficiently learn from user various
implicit feedback through a multi-task learning framework. By deploying the
$\text{PR}^2$ in production system, we have achieved the most remarkable user
engagement improvements in recent years: a 10.2% increase in CTR@10, a notable
20% surge in video watch time, and a 1.6% uplift of search DAU. We believe the
practical insights presented in this work are valuable especially for building
and improving personalized search systems for the short video platforms.