Fast, lenient and accurate: Building personalized instant search experience at LinkedIn

摘要

Instant search has become a common part of the search experience in most popular search engines and social networking websites. The goal is to provide instant feedback to the user in terms of query completions (“instant suggestions”) or directly provide search results (“instant results”) as the user is typing their query. In this paper, we describe the challenges that we faced while delivering the instant search experience at LinkedIn, and present techniques that we developed to overcome them. We discuss three aspects of instant search - performance, tolerance to user errors and accuracy. On the performance side, we discuss our inverted index ordering scheme, which when combined with query rewriting and early termination techniques, helped us significantly reduce latency while maintaining good search recall. We describe our method to handle hard to spell or misspelled names using name clusters. We also present ranking methods that leverage the structured nature of queries and documents at LinkedIn. All the methods described have been fully deployed in production, and have helped us significantly improve the search experience for our members. Specifically, we increased the number of queries served via typeahead by 39.53%.