User Intent Inference for Web Search and Conversational Agents
This work addresses the problem of ambiguous user queries for developers of conversational agents and search engines, but it is incremental as it builds on existing methods.
The thesis tackles user intent inference for conversational agents and web search by proposing models that incorporate entity information and conversation-context clues for utterance classification, and extending state-of-the-art methods to e-commerce for joint intent and product category prediction, with evaluation on real e-commerce queries.
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.