CS Seminar
| Title: Knowledge-Aware User Intent Inference for Web Search and Conversational Agents |
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| Seminar: Computer Science |
| Speaker: Ali Ahmadvand, Emory University |
| Contact: Eugene Agichtein, eugene.agichtein@emory.edu |
| Date: 2021-11-24 at 2:00PM |
| Venue: https://zoom.us/j/9912158487?pwd=aURCWjVpY1BmVzBaSDB6QktmZ2xvZz09 |
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| Abstract: User intent inference is a critical step in designing intelligent information systems (e.g., conversational agents and e-commerce search engines). Accurate user intent inference improves user experience and satisfaction, but is a challenging task since user utterances or queries can be short, ambiguous, and contextually dependent. Moreover, in an e-commerce setting, the collected datasets are often labeled by weak supervision (e.g., click-through data), resulting in an imbalanced and sparse dataset. To address these problems, my dissertation proposes integrating entity knowledge-bases, conversation context, and user profile information to improve user intent inference for conversational agents. Additionally, I investigate joint learning, product taxonomies, and unlabeled domain-specific corpora (e.g., catalog) to improve query intent inference in e-commerce search. To evaluate the proposed models, I examine the user intent inference for two main settings: 1) open-domain conversational agents and 2) e-commerce search engines. The conversational agent research is evaluated on conversations collected from real users as part of Amazon Alexa Prize competitions, and the e-commerce efforts use real query logs collected from The Home Depot's search engine. My dissertation shows that leveraging entity knowledge-base, conversation context, and user profile information accounts for most improvements for the conversational setting. The results demonstrate that the proposed models significantly enhance topic classification accuracy by 15% and dialogue act accuracy by 8% for conversational agents. For e-commerce search, the dissertation shows that joint-learning, product taxonomies, and unlabeled domain-specific corpora can significantly improve intent inference accuracy. The proposed models improve the performance of the top-1 retrieved documents by 6%-8% on standard metrics for e-commerce search. The results in both settings offer a significant improvement over state-of-the-art deep learning methods. The insights and findings in this dissertation suggest a promising direction for developing the user intent inference in both open-domain conversational agents and e-commerce search. |
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