國立虎尾科技大學 |

Intent Prediction and User Preference Modeling in Conversational Search and Recommendation /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Intent Prediction and User Preference Modeling in Conversational Search and Recommendation // Harshita Jagdish Sahijwani.
Author:	Sahijwani, Harshita Jagdish,
Description:	1 electronic resource (105 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: A.
Contained By:	Dissertations Abstracts International86-05A.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31718783
ISBN:	9798384478546

Intent Prediction and User Preference Modeling in Conversational Search and Recommendation /
Sahijwani, Harshita Jagdish,

Intent Prediction and User Preference Modeling in Conversational Search and Recommendation /Harshita Jagdish Sahijwani. - 1 electronic resource (105 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: A.

Conversational systems have emerged as potent tools for increasing the accessibility of user-facing applications, in particular, search and recommendation. This dissertation addresses two key challenges in conversational systems: intent prediction and user preference modeling.Identifying the intent of each user utterance in conversational systems is a crucial step for all subsequent language understanding and response tasks. The high cost of collecting conversational training data makes the task of intent prediction challenging. The first main research question addressed in this dissertation is: Can we use external knowledge and synthetic data to improve intent prediction? I propose methods for knowledge-aware intent prediction in three settings, including open-domain social bots, conversational information elicitation systems, and web-based domain-specific search systems. In addition, I study the impact of synthetic data on intent prediction in these systems.User preference modeling is another essential part of effective conversational systems. The second main research question addressed in this dissertation is: Can we anticipate the user's next topic of interest by constructing a user profile using conversation context? I propose methods to represent the user based on the conversation history. Moreover, a sequence modeling approach is proposed to predict the user's next topic of interest in conversational systems.Despite the capacity of large language models to implicitly perform end-to-end intent detection and user preference modeling, they are not universally applicable. They cannot be used with private data. Moreover, modular systems with specialized components allow for more interpretability and control over the system. Systems with modules for intent detection and user preference modeling are thus still relevant.Together, the proposed methods enable a better understanding of the user's immediate needs and long-term preferences in all types of conversational systems. The findings of this research hold implications for improving the accuracy and performance of conversational search and recommender systems.

English

ISBN: 9798384478546Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Information retrieval

Intent Prediction and User Preference Modeling in Conversational Search and Recommendation /
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