語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Intent Prediction and User Preference Modeling in Conversational Search and Recommendation /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Intent Prediction and User Preference Modeling in Conversational Search and Recommendation // Harshita Jagdish Sahijwani.
作者:
Sahijwani, Harshita Jagdish,
面頁冊數:
1 electronic resource (105 pages)
附註:
Source: Dissertations Abstracts International, Volume: 86-05, Section: A.
Contained By:
Dissertations Abstracts International86-05A.
標題:
Information science. -
電子資源:
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:
561178
Information science.
Subjects--Index Terms:
Information retrieval
Intent Prediction and User Preference Modeling in Conversational Search and Recommendation /
LDR
:03638nam a22004453i 4500
001
1157887
005
20250603111435.5
006
m o d
007
cr|nu||||||||
008
250804s2024 miu||||||m |||||||eng d
020
$a
9798384478546
035
$a
(MiAaPQD)AAI31718783
035
$a
(MiAaPQD)jm214q447
035
$a
AAI31718783
040
$a
MiAaPQD
$b
eng
$c
MiAaPQD
$e
rda
100
1
$a
Sahijwani, Harshita Jagdish,
$e
author.
$3
1484175
245
1 0
$a
Intent Prediction and User Preference Modeling in Conversational Search and Recommendation /
$c
Harshita Jagdish Sahijwani.
264
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2024
300
$a
1 electronic resource (105 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Dissertations Abstracts International, Volume: 86-05, Section: A.
500
$a
Advisors: Murdock, Vanessa.
502
$b
Ph.D.
$c
Emory University
$d
2024.
520
$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.
546
$a
English
590
$a
School code: 0665
650
4
$a
Information science.
$3
561178
650
4
$a
Computer science.
$3
573171
653
$a
Information retrieval
653
$a
Conversational systems
653
$a
Intent prediction
653
$a
User representation
653
$a
User preference modeling
690
$a
0800
690
$a
0984
690
$a
0723
710
2
$a
Emory University.
$b
Computer Science and Informatics.
$3
1195855
720
1
$a
Murdock, Vanessa
$e
degree supervisor.
773
0
$t
Dissertations Abstracts International
$g
86-05A.
790
$a
0665
791
$a
Ph.D.
792
$a
2024
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31718783
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入