Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search
This addresses relevance issues in social network search, but it is an incremental improvement with preliminary methods.
The paper tackled the problem of uncontrollable relevance in embedding-based retrieval for social network search, identifying integrity and junkiness failures and proposing simple inference methods that improved offline NDCG and online A/B test metrics.
Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc. While the approach has demonstrated its efficacy in tasks like semantic matching and contextual search, it is plagued by the problem of uncontrollable relevance. In this paper, we conduct an analysis of embedding-based retrieval launched in early 2021 on our social network search engine, and define two main categories of failures introduced by it, integrity and junkiness. The former refers to issues such as hate speech and offensive content that can severely harm user experience, while the latter includes irrelevant results like fuzzy text matching or language mismatches. Efficient methods during model inference are further proposed to resolve the issue, including indexing treatments and targeted user cohort treatments, etc. Though being simple, we show the methods have good offline NDCG and online A/B tests metrics gain in practice. We analyze the reasons for the improvements, pointing out that our methods are only preliminary attempts to this important but challenging problem. We put forward potential future directions to explore.