Improving Diversity in Conversational Recipe Recommender through Dynamic Critiquing

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Abbas, F. (2022). Improving Diversity in Conversational Recipe Recommender through Dynamic Critiquing. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Diet diversification has been shown both to improve nutritional health outcomes and to promote greater enjoyment in food consumption. Conversational Recommender Systems (CRS) have a rich history in direct recommendation of recipes and meal planning, as well as conversational exploration of the possibilities for new food items. However, limited attention has been given to incorporating diversity outcomes as a primary factor in conversational critique for exploration. Critiquing as a method of feedback in recommendation has proven effective for conversational interactions, and diversifying recommended items during the exploration can help users broaden their food options, which critiquing alone may not achieve. All of these aspects together are important elements for recommender applications in the food domain. This dissertation explores incorporating diversity in a critique-based conversational recommender system to support diet diversification. Recommender systems are known to support the task of exploitation while diversity supports the task of exploration. The research in this dissertation employs a conversational recommender approach to help maintain this balance --- enabling exploration through critiquing and exploitation by selecting the closest matching recommendations to the user profile. To enable this balance this dissertation introduces an interactive critique-based conversational recipe recommender approach called \textit{DiversityBite}, a novel way of dynamically generating critique during recipe recommendation. This dissertation presents three studies --- one simulation study and two user studies --- to show the potential of using dynamic critique in increasing diversity. These studies investigate how the proposed \textit{DiversityBite} approach can improve diversity in recipe recommendation. The contributions of this dissertation are: (i) Development and evaluation of a novel approach of dynamic diversity-focused critique for conversational recommender system, (ii) Applying dynamic diversity-focused critique in recipes domain to support diet diversification while exploring, and (iii) Identification of recipe features that are helpful in finding diverse recipes using dynamic critique. The results show that diversity can be increased using conversational recommender using dynamic-focused critique.

Details
Author

Abbas, Fakhri

Title

Improving Diversity in Conversational Recipe Recommender through Dynamic Critiquing

Physical Description

1 online resource (108 pages) : PDF

Date

2022

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Diet diversification has been shown both to improve nutritional health outcomes and to promote greater enjoyment in food consumption. Conversational Recommender Systems (CRS) have a rich history in direct recommendation of recipes and meal planning, as well as conversational exploration of the possibilities for new food items. However, limited attention has been given to incorporating diversity outcomes as a primary factor in conversational critique for exploration. Critiquing as a method of feedback in recommendation has proven effective for conversational interactions, and diversifying recommended items during the exploration can help users broaden their food options, which critiquing alone may not achieve. All of these aspects together are important elements for recommender applications in the food domain. This dissertation explores incorporating diversity in a critique-based conversational recommender system to support diet diversification. Recommender systems are known to support the task of exploitation while diversity supports the task of exploration. The research in this dissertation employs a conversational recommender approach to help maintain this balance --- enabling exploration through critiquing and exploitation by selecting the closest matching recommendations to the user profile. To enable this balance this dissertation introduces an interactive critique-based conversational recipe recommender approach called \textit{DiversityBite}, a novel way of dynamically generating critique during recipe recommendation. This dissertation presents three studies --- one simulation study and two user studies --- to show the potential of using dynamic critique in increasing diversity. These studies investigate how the proposed \textit{DiversityBite} approach can improve diversity in recipe recommendation. The contributions of this dissertation are: (i) Development and evaluation of a novel approach of dynamic diversity-focused critique for conversational recommender system, (ii) Applying dynamic diversity-focused critique in recipes domain to support diet diversification while exploring, and (iii) Identification of recipe features that are helpful in finding diverse recipes using dynamic critique. The results show that diversity can be increased using conversational recommender using dynamic-focused critique.

Genre

doctoral dissertations

Subjects--Topics

Information science

Degree

Ph.D.

Keywords

Conversational
Dyanmic Critique
Recipe Recommender
Recommender System

Subject Area

Computer Science

Advisor(s)

Wilson, David
Najjar, Nadia

Committee Members

Maher, Mary Lou
Grace, Kazjon
Yang, Jing

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2022.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Abbas_uncc_0694D_13079

Permalink

http://hdl.handle.net/20.500.13093/etd:2925