Cataldo Musto
Christoph Trattner
Alain Starke
Giovanni Semeraro
ABSTRACT
Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits.
In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.
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- Robin Burke. 2000. Knowledge-based recommender systems. Encyclopedia of library and information systems, Vol. 69, Supplement 32 (2000), 175--186.Google Scholar
- Marco De Gemmis, Leo Iaquinta, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro. 2010. Learning preference models in recommender systems. In Preference Learning. Springer, 387--407.Google Scholar
- Michael D Ekstrand and Martijn C Willemsen. 2016. Behaviorism is not enough: better recommendations through listening to users. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 221--224.Google ScholarDigital Library
- David Elsweiler, Christoph Trattner, and Morgan Harvey. 2017. Exploiting food choice biases for healthier recipe recommendation. In Proc. of SIGIR '17.Google ScholarDigital Library
- Jill Freyne and Shlomo Berkovsky. 2010. Intelligent food planning: personalized recipe recommendation. In Proceedings of the 15th international conference on Intelligent user interfaces. 321--324.Google ScholarDigital Library
- Yu Liang. 2019. Recommender system for developing new preferences and goals. In Proceedings of the 13th ACM Conference on Recommender Systems. ACM, 611--615.Google ScholarDigital Library
- Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2019. Linked open data-based explanations for transparent recommender systems. International Journal of Human-Computer Studies, Vol. 121 (2019), 93--107.Google ScholarCross Ref
- Cataldo Musto, Giovanni Semeraro, Cosimo Lovascio, Marco de Gemmis, and Pasquale Lops. 2018. A framework for holistic user modeling merging heterogeneous digital footprints. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. 97--101.Google ScholarDigital Library
- James O Prochaska. 2008. Decision making in the transtheoretical model of behavior change. Medical decision making, Vol. 28, 6 (2008), 845--849.Google Scholar
- Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K Lam, Sean M McNee, Joseph A Konstan, and John Riedl. 2002. Getting to know you: learning new user preferences in recommender systems. In Proceedings of the 7th international conference on Intelligent user interfaces. 127--134.Google ScholarDigital Library
- Markus Rokicki, Eelco Herder, Tomasz Ku'smierczyk, and Christoph Trattner. 2016. Plate and prejudice: Gender differences in online cooking. In Proceedings of the 2016 conference on user modeling adaptation and personalization. 207--215.Google ScholarDigital Library
- Hanna Sch"afer, Santiago Hors-Fraile, Raghav Pavan Karumur, André Calero Valdez, Alan Said, Helma Torkamaan, Tom Ulmer, and Christoph Trattner. 2017. Towards health (aware) recommender systems. In Proceedings of the 2017 international conference on digital health. ACM, 157--161.Google Scholar
- Hanna Sch"afer and Martijn C Willemsen. 2019. Rasch-based tailored goals for nutrition assistance systems. In Proceedings of the 24th International Conference on Intelligent User Interfaces. ACM, 18--29.Google Scholar
- Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov, and Marius Kaminskas. 2015. Music recommender systems. In Recommender systems handbook. Springer, 453--492.Google Scholar
- Benjamin Scheibehenne, Linda Miesler, and Peter M Todd. 2007. Fast and frugal food choices: Uncovering individual decision heuristics. Appetite, Vol. 49, 3 (2007), 578--589.Google ScholarCross Ref
- Alain Starke. 2019. RecSys Challenges in achieving sustainable eating habits. In HealthRecSys'19: Proceedings of the 4th Workshop on Health Recommender Systems. ACM, 29--30.Google Scholar
- Alain Starke, Martijn Willemsen, and Chris Snijders. 2017. Effective user interface designs to increase energy-efficient behavior in a Rasch-based energy recommender system. In Proceedings of the Eleventh ACM Conference on Recommender Systems. 65--73.Google ScholarDigital Library
- Alain D. Starke, Martijn C. Willemsen, and Chris Snijders. 2020. With a Little Help from My Peers: Depicting Social Norms in a Recommender Interface to Promote Energy Conservation. In Proceedings of the 25th International Conference on Intelligent User Interfaces (Cagliari, Italy) (IUI '20). Association for Computing Machinery, New York, NY, USA, 568--578. https://doi.org/10.1145/3377325.3377518Google ScholarDigital Library
- Napat Suksom, Marut Buranarach, Ye Myat Thein, Thepchai Supnithi, and Ponrudee Netisopakul. 2010. A knowledge-based framework for development of personalized food recommender system. In Proc. of the 5th Int. Conf. on Knowledge, Information and Creativity Support Systems.Google Scholar
- Thi Ngoc Trang Tran, Müslüm Atas, Alexander Felfernig, and Martin Stettinger. 2018. An overview of recommender systems in the healthy food domain. Journal of Intelligent Information Systems, Vol. 50, 3 (2018), 501--526.Google ScholarDigital Library
- Christoph Trattner and David Elsweiler. 2017a. Food recommender systems: important contributions, challenges and future research directions. arXiv preprint arXiv:1711.02760 (2017).Google Scholar
- Christoph Trattner and David Elsweiler. 2017b. Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems. In Proc. of WWW '17. 489--498.Google ScholarDigital Library
- Christoph Trattner and Dietmar Jannach. 2019. Learning to recommend similar items from human judgments. User Modeling and User-Adapted Interaction (2019), 1--49.Google Scholar
- Christoph Trattner, Dominik Moesslang, and David Elsweiler. 2018. On the predictability of the popularity of online recipes. EPJ Data Science, Vol. 7, 1 (2018).Google ScholarCross Ref
- Alexandra Uitdenbogerd and R Schyndel. 2002. A review of factors affecting music recommender success. In ISMIR 2002, 3rd International Conference on Music Information Retrieval. IRCAM-Centre Pompidou, 204--208.Google Scholar
Index Terms
- Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Models
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