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We evaluated embedding-based retrieval on verticals for Facebook Search with significant metrics gains observed in online A/B experiments

Embedding-based Retrieval in Facebook Search

KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2553-2561, (2020)

被引用36|浏览241
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摘要

Search in social networks such as Facebook poses different challenges than in classical web search: besides the query text, it is important to take into account the searcher's context to provide relevant results. Their social graph is an integral part of this context and is a unique aspect of Facebook search. While embedding-based retriev...更多

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简介
  • Search engines have been an important tool to help people access the huge amount of information online.
  • Since it is difficult to accurately compute the search intent from query text and represent the semantic meaning of documents, search techniques are mostly based on various term matching methods [1], which performs well for the cases that keyword match can address.
  • Due to the huge success of this technique in other domains including computer vision and recommendation system, it has been an active research topic in information retrieval community and search engine industry as the generation search technology [13]
重点内容
  • Search engines have been an important tool to help people access the huge amount of information online
  • We evaluated embedding-based retrieval on verticals for Facebook Search with significant metrics gains observed in online A/B experiments
  • We discuss the techniques we developed on later-stage optimization to unleash the power from embedding-based retrieval end to end in Section 5
  • While it is important to tune ANN algorithms and parameters offline to get a reasonable understanding of perf vs. recall trade-off, we found it is important to deploy several configs of the ANN algorithms and parameters online to get a better understanding of the perf impact from embedding-based retrieval to the real system
  • We presented our approach of unified embedding to model semantics for social search, and the implementation of embedding-based retrieval in a classical inverted index based search system
  • The successful deployment of embedding-based retrieval in production opens a door for sustainable improvement of retrieval quality by leveraging the latest semantic embedding learning techniques
结果
  • Search is a multi-stage ranking system where retrieval is the first stage, followed by various stages of ranking and filtering models.
  • The authors incorporated embeddings into ranking layers and built a training data feedback loop to actively learn to identify those good and bad results from embedding-based retrieval.
  • The authors discuss the techniques the authors developed on later-stage optimization to unleash the power from embedding-based retrieval end to end in Section 5.
  • The authors dedicate Section 6 to selected topics on advanced modeling techniques, followed by conclusions in Section 7
结论
  • It has long term benefits to introduce semantic embeddings into search retrieval to address the semantic matching issues by leveraging the advancement on deep learning research.
  • It is a highly challenging problem due to the modeling difficulty, system implementation and cross-stack optimization complexity, especially for a large-scale personalized social search engine.
  • The authors introduced the progress and learnings from the first step along this direction, especially on hard mining and embedding ensemble
表格
  • Table1: Group Embedding Improvement with Feature Engineering
  • Table2: Top Similar Groups Before and After Adding Location Embeddings
  • Table3: Impact of Product Quantization on 1-Recall@10 for 128-Dimension Embedding
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引用论文
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