Diet diversification has been shown both to improve nutritional health outcomes and to promote greater enjoyment in food consumption. Conversational Recommender Systems (CRS) has a rich history in direct recommendation of recipes and meal planning, as well as conversational exploration of the possibilities for new food items. But more limited attention has been given to incorporating diversity outcomes as a primary factor in conversational critique for exploration. Critiquing as a method of feedback 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.
\par 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. Using a conversational recommender, this dissertation maintains this balance by enabling the exploration through critiquing, and maintains the exploitation by selecting the closest recommendation to the user profile. To enable this balance this dissertation introduces an interactive critique based conversational recipe recommender system called \textit{DiversityBite}, a novel way of dynamically generating critique during recipe recommendation.
\par The contributions of this dissertation are: (i) Development 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. This study reports on three studies to show the potential of using dynamic critique in increasing diversity. The user studies considered for this dissertation are simulation study, and two user studies. These studies investigate if \textit{DiversityBite} can improve diversity in recipe recommendation.