User Intent Inference for Web Search and Conversational Agents

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 ecommerce 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.

Fintech Agents: Technologies and Theories

“…Such agents can handle questions that are ambiguous or new based on natural language processing. The more they interact with users, the more information and accuracy they gather [71,72]. Examples of fintech agents that can self-learn are HSBC Bank's virtual assistant Amy, introduced earlier, which has an in-built customer feedback mechanism to enhance knowledge over time and answer complex queries [70].…”