Deep Search Query Intent Understanding
This work addresses the need for better search engine performance by improving query intent prediction, but it appears incremental as it builds on existing deep learning components without introducing a new paradigm.
The paper tackled the problem of understanding user query intent in search engines by developing a comprehensive learning framework for predicting intent during typeahead and for complete queries, resulting in effective and scalable methods as shown in offline and online A/B tests.
Understanding a user's query intent behind a search is critical for modern search engine success. Accurate query intent prediction allows the search engine to better serve the user's need by rendering results from more relevant categories. This paper aims to provide a comprehensive learning framework for modeling query intent under different stages of a search. We focus on the design for 1) predicting users' intents as they type in queries on-the-fly in typeahead search using character-level models; and 2) accurate word-level intent prediction models for complete queries. Various deep learning components for query text understanding are experimented. Offline evaluation and online A/B test experiments show that the proposed methods are effective in understanding query intent and efficient to scale for online search systems.