User Intent Inference for Web Search and Conversational Agents

TL;DR

This paper explores methods for inferring user intent in web search and conversational agents, focusing on classifying utterances and queries to improve natural language understanding and user experience.

Contribution

It introduces novel models incorporating entity and context information for intent classification and extends intent prediction techniques to the e-commerce domain.

Findings

Proposed models improve intent classification accuracy.

Extended intent prediction methods for e-commerce queries.

Evaluation on real e-commerce search data demonstrates effectiveness.

Abstract

User intent understanding is a crucial step in designing both conversational agents and search engines. Detecting or inferring user intent is challenging, since the user utterances or queries can be short, ambiguous, and contextually dependent. To address these research challenges, my thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines, focusing on the e-commerce domain. To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances. For the second research topic, I plan to extend the existing state of the art methods in Web search intent prediction to the e-commerce domain, via: 1) Developing a joint learning model to predict search queries' intents and the product categories associated with them, 2) Discovering new hidden users' intents. All the models will be evaluated on the real queries available from a major e-commerce site search engine. The results from these studies can be leveraged to improve performance of various tasks such as natural language understanding, query scoping, query suggestion, and ranking, resulting in an enriched user experience.