GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Social navigation of food recipes
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Intelligent food planning: personalized recipe recommendation
Proceedings of the 15th international conference on Intelligent user interfaces
Deriving a recipe similarity measure for recommending healthful meals
Proceedings of the 16th international conference on Intelligent user interfaces
Recommending food: reasoning on recipes and ingredients
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Estimating importance of implicit factors in e-commerce recommender systems
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Biologeek, an intelligent system for service mashups tuned for recipe processing and rendering
Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities
Rating Bias and Preference Acquisition
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Food and diet are complex domains for recommender technology, but the need for systems that assist users in embarking on and engaging with healthy living programs has never been more real. One key to sustaining long term engagement with eHealth services is the provision of tools, which assist and train users in planning correctly around the areas of diet and exercise. These tools require an understanding of user reasoning as well as user needs and are ideal application areas for recommender and personalization technologies. Here, we report on a large scale analysis of real user ratings on a set of recipes in order to judge the applicability and practicality of a number of personalization algorithms. Further to this, we report on apparent user reasoning patterns uncovered in rating data supplied for recipes and suggest ways to exploit this reasoning understanding in the recommendation process.