Abstract
Diet diversification has been shown both to improve nutritional health outcomes and to promote greater enjoyment in food consumption. CBR 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 exploration can help users broaden their food options, which critiquing alone may not achieve. And all of these aspects together are important elements for recommender applications in the food domain. In this paper, we introduce DiversityBite, a novel CBR approach that brings together critique and diversity to support conversational recommendation in the recipe domain. Our initial user study evaluation shows that DiversityBite is effective in promoting meal plan diversity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbas, F.G., Najjar, N., Wilson, D.: Critique generation to increase diversity in conversational recipe recommender system. In: The International FLAIRS Conference Proceedings, vol. 34 (2021)
Abbas, F.G., Najjar, N., Wilson, D.: Increasing diversity through dynamic critique in conversational recipe recommendations. In: Proceedings of the 13th Workshop on Multimedia for Cooking and Eating Activities (2021)
Anderson, C.: A survey of food recommenders. arXiv preprint arXiv:1809.02862 (2018)
Bradley, K., Smyth, B.: Improving recommendation diversity. In: Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland. Citeseer (2001)
Bridge, D., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. Knowl. Eng. Rev. 20(3), 315–320 (2005)
Burke, R.D., Hammond, K.J., Young, B.C.: Knowledge-based navigation of complex information spaces. In: Proceedings of the National Conference on Artificial Intelligence, vol. 462 (1996)
Drescher, L.S., Thiele, S., Mensink, G.B.: A new index to measure healthy food diversity better reflects a healthy diet than traditional measures. J. Nutr. 137(3), 647–651 (2007)
Elsweiler, D., Harvey, M., Ludwig, B., Said, A.: Bringing the “healthy” into food recommenders. In: DMRS (2015)
Fleder, D.M., Hosanagar, K.: Recommender systems and their impact on sales diversity. In: Proceedings of the 8th ACM Conference on Electronic Commerce (2007)
Freyne, J., Berkovsky, S.: Intelligent food planning: personalized recipe recommendation. In: Proceedings of the 15th International Conference on Intelligent User Interfaces (2010)
Grace, K., Maher, M.L., Wilson, D., Najjar, N.: Personalised specific curiosity for computational design systems. In: Gero, J.S. (ed.) Design Computing and Cognition ’16, pp. 593–610. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44989-0_32
Griffiths, E.J., Dooley, D.M., Buttigieg, P.L., Hoehndorf, R., Brinkman, F.S., Hsiao, W.W.: FoodON: a global farm-to-fork food ontology. In: ICBO/BioCreative (2016)
Hu, R., Pu, P.: Helping users perceive recommendation diversity. In: DiveRS@ RecSys (2011)
Jannach, D., Manzoor, A., Cai, W., Chen, L.: A survey on conversational recommender systems. arXiv preprint arXiv:2004.00646 (2020)
Kahn, B.E., Wansink, B.: The influence of assortment structure on perceived variety and consumption quantities. J. Consum. Res. 30(4), 519–533 (2004)
Kelly, J.P., Bridge, D.: Enhancing the diversity of conversational collaborative recommendations: a comparison. Artif. Intell. Rev. 25(1–2), 79–95 (2006)
Knijnenburg, B.P., Sivakumar, S., Wilkinson, D.: Recommender systems for self-actualization. In: Proceedings of the 10th ACM Conference on Recommender Systems (2016)
McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: On the dynamic generation of compound critiques in conversational recommender systems. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 176–184. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27780-4_21
McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: An analysis of critique diversity in case-based recommendation. In: FLAIRS Conference (2005)
McCarthy, K., Reilly, J., Smyth, B., Mcginty, L.: Generating diverse compound critiques. Artif. Intell. Rev. 24(3), 339–357 (2005)
McGinty, L., Smyth, B.: On the role of diversity in conversational recommender systems. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 276–290. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45006-8_23
McSherry, D.: Diversity-conscious retrieval. In: Craw, S., Preece, A. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 219–233. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46119-1_17
van Pinxteren, Y., Geleijnse, G., Kamsteeg, P.: Deriving a recipe similarity measure for recommending healthful meals. In: Proceedings of the 16th International Conference on Intelligent User Interfaces (2011)
Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Dynamic critiquing. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 763–777. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28631-8_55
Sajadmanesh, S., et al.: Kissing cuisines: exploring worldwide culinary habits on the web. In: Proceedings of the 26th International Conference on World Wide Web Companion (2017)
Smyth, B., McClave, P.: Similarity vs. diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 347–361. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44593-5_25
Smyth, B., McGinty, L.: An analysis of feedback strategies in conversational recommenders. In: the Fourteenth Irish Artificial Intelligence and Cognitive Science Conference (AICS 2003). Citeseer (2003)
Vargas, S., Baltrunas, L., Karatzoglou, A., Castells, P.: Coverage, redundancy and size-awareness in genre diversity for recommender systems. In: Proceedings of the 8th ACM Conference on Recommender Systems (2014)
Zeyen, C., Hoffmann, M., Müller, G., Bergmann, R.: Considering nutrients during the generation of recipes by process-oriented case-based reasoning. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 464–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_31
Zeyen, C., Müller, G., Bergmann, R.: Conversational retrieval of cooking recipes. In: ICCBR (Workshops) (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Abbas, F., Najjar, N., Wilson, D. (2021). The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds) Case-Based Reasoning Research and Development. ICCBR 2021. Lecture Notes in Computer Science(), vol 12877. Springer, Cham. https://doi.org/10.1007/978-3-030-86957-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-86957-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86956-4
Online ISBN: 978-3-030-86957-1
eBook Packages: Computer ScienceComputer Science (R0)