Facebook Content Search: Efficient and Effective Adapting Search on A Large Scale
Authors: 
Xiangyu Niu, Yu-Wei Wu, Xiao Lu, Gautam Nagpal, Philip Pronin, Kecheng Hao, Zhen Liao, Guangdeng LiaoAuthors Info & Claims
Pages 3290 - 3294
Abstract
Facebook content search is a critical channel that enables people to discover the best content to deepen their engagement with friends and family, creators, and communities. Building a highly personalized search engine to serve billions of daily active users to find the best results from a large scale of candidates is a challenging task. The search engine must take multiple dimensions into consideration, including different content types, different query intents, and user social graph, etc. In this paper, we discuss the challenges of Facebook content search in depth, and then describe our novel approach to efficiently handling a massive number of documents with advanced query understanding, retrieval, and machine learning techniques. The proposed system has been fully verified and applied to the production system of Facebook Search, which serves billions of users.
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In this video, we discuss the challenges of Facebook content search, and then describes our novel approach to efficiently handling a massive number of documents with advanced query understanding, retrieval, and machine learning techniques.
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Information
Published In
July 2023
3567 pages
ISBN:9781450394086
DOI:10.1145/3539618
- General Chairs:
- Hsin-Hsi Chen,
- Wei-Jou (Edward) Duh,
- Hen-Hsen Huang,
- Program Chairs:
- Makoto P. Kato,
- Josiane Mothe,
- Barbara Poblete
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Published: 18 July 2023
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SIGIR '23
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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 23 - 27, 2023
Taipei, Taiwan
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