Instant Search

Autor: Abhimanyu Lad, Ganesh Venkataraman, Viet Ha-Thuc, Dhruv Arya
Rok vydání: 2016
Předmět:
Zdroj: SIGIR
DOI: 10.1145/2911451.2914806
Popis: 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. The need for instant search has been further amplified by the proliferation of mobile devices and services like Siri and Google Now that aim to address the user's information need as quickly as possible. Examples of instant results include web queries like "weather san jose" (which directly provides the current temperature), social network queries like searching for someone's name on Facebook or LinkedIn (which directly provide the people matching the query). In each of these cases, instant search constitutes a superior user experience, as opposed to making the user complete their query before the system returns a list of results on the traditional search engine results page (SERP). We consider instant search experience to be a combination of instant results and instant suggestions, with the goal of satisfying the user's information need as quickly as possible with minimal effort on the part of the user. We first present the challenges involved in putting together an instant search solution at scale, followed by a survey of IR and NLP techniques that can be used to address them. We will also conduct a hands-on session aimed at putting together an end-to-end instant search system using open source tools and publicly available data sets. These tools include typeahead.js from Twitter for the frontend and Lucene/elasticsearch for the backend. We present techniques for prefix-based retrieval as well as injecting custom ranking functions into elasticsearch. For the search index, we will use the dataset made available by Stackoverflow. This tutorial is aimed at both researchers interested in knowing about retrieval techniques used for instant search as well as practitioners interested in deploying an instant search system at scale. The authors have worked extensively on building and scaling LinkedIn's instant search experience. To the best of our knowledge, this is the first tutorial that covers both theoretical and practical aspects of instant search.
Databáze: OpenAIRE
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