Age-related Difference in Conversational Search Behavior: Preliminary Findings

Xing, Zhaopeng; Yuan, Xiaojun; Mostafa, Javed

doi:10.1145/3498366.3505830

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…This confirms the results of other studies, which state that people with a higher education level are more likely to retrieve information using smartphones 10 and typically search for information on the Internet, whereas older adults generally rely on simpler and more traditional search strategies, such as the newspaper or television programs. 11 , 12 All the patients using this counselling program after discharge reported being satisfied and very satisfied with this simple conversation agent. Customization of these existing free modules in communication apps for patient education would be a cost-effective approach to improving patient understanding and satisfaction.…”

Section: Discussionmentioning

confidence: 94%

Free interactive counselling program in a mobile communication application for improving health education on indwelling ureteric stents after ureterorenoscopic lithotripsy: An observational study

et al. 2022

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Objective This study examines the potential benefit of an interactive counselling program via a mobile application (app), which can instantly provide patients with the necessary information and correct response regarding their condition. Methods We designed a free ‘Ureteric Stent Interactive Program’ for patients receiving ureterorenoscopic lithotripsy and provided the program to interested patients. Patient data were collected from medical records and depending on whether patients used our program, they were divided into two groups: ‘program-user’ and ‘non-user’. The differences between the groups were analysed using Fisher’s exact tests. Results Of the 70 patients, 50 elected to use the program. The program-user group was significantly younger (<60 years: 74% vs 15%, P<0.001) and had higher education levels (40% vs 5%, P = 0.004). All 50 patients in the program-user group reported being satisfied (32%) or very satisfied (68%) with the program. Patients over 60 years were significantly more satisfied with program (35.5% vs 6.3%, P = 0.04). Conclusions Younger patients with high education levels were more likely to use the app and improve their health knowledge. Using the program resulted in high satisfaction, especially among older patients. This study demonstrates the benefits of interactive application for educating patients regarding their health.

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Section: Discussionmentioning

confidence: 94%

Free interactive counselling program in a mobile communication application for improving health education on indwelling ureteric stents after ureterorenoscopic lithotripsy: An observational study

et al. 2022

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“…An area related to CRS is conversational search systems [74,80]. Research work in this area tend to focus on resolving ambiguity in natural language [12,59,79], on open-domain large-scale document retrieval [11,29,60,61], and often do not to utilize historic user-item interactions [28,40,67].…”

Section: Related Work 21 Conversational Recommendation Systemsmentioning

confidence: 99%

Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference

Zhang¹,

Li²,

Liu³

et al. 2022

Preprint

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Conversational recommendation system (CRS) is emerging as a user-friendly way to capture users' dynamic preferences over candidate items and attributes. Multi-shot CRS is designed to make recommendations multiple times until the user either accepts the recommendation or leaves at the end of their patience. Existing works are trained with reinforcement learning (RL), which may suffer from unstable learning and prohibitively high demands for computing. In this work, we propose a simple and efficient CRS, MInimalist Non-reinforced Interactive COnversational Recommender Network (Minicorn). Minicorn models the epistemic uncertainty of the estimated user preference and queries the user for the attribute with the highest uncertainty. The system employs a simple network architecture and makes the query-vs-recommendation decision using a single rule. Somewhat surprisingly, this minimalist approach outperforms state-of-the-art RL methods on three realworld datasets by large margins. We hope that Minicorn will serve as a valuable baseline for future research.

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“…An area related to CRS is conversational search systems [139,140]. Research work in this area tends to focus on resolving ambiguity in natural language [141][142][143], on open-domain large-scale document retrieval [144][145][146][147], and often do not to utilize historic user-item interactions [148][149][150].…”

Section: Conversational Recommendationmentioning

confidence: 99%

Transferable and curious AI in sequential recommendation

Zhang¹

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Reaching the end of my PhD, I would like to express my sincerest gratitude to all those who have guided me, helped me, and encouraged me during this bittersweet journey. First and foremost, I am tremendously grateful to my supervisor, Prof. Chunyan Miao for the kind support and mentorship throughout my PhD. Her kind support and keen insight were key factors in making my research possible. I am very glad to have the chance to collaborate with Prof. Boyang Li and Dr. Yong Liu. During the discussions with them, I learned how to do research with critical thinking and a rigorous attitude. I would like to thank my committee members, Prof. Han Yu and Prof. Lihui Chen, for their guidance. I am also grateful for the teaching assistant opportunities provided by Dr. Zhiqi Shen.

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Age-related Difference in Conversational Search Behavior: Preliminary Findings

Cited by 3 publications

References 35 publications

Free interactive counselling program in a mobile communication application for improving health education on indwelling ureteric stents after ureterorenoscopic lithotripsy: An observational study

Free interactive counselling program in a mobile communication application for improving health education on indwelling ureteric stents after ureterorenoscopic lithotripsy: An observational study

Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference

Transferable and curious AI in sequential recommendation

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