Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Intelligent food planning: personalized recipe recommendation
Proceedings of the 15th international conference on Intelligent user interfaces
Application of hybrid recommendation in web-based cooking assistant
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Creating personalized digital human models of perception for visual analytics
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Recipe recommendation: accuracy and reasoning
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Personalized techniques for lifestyle change
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Rating Bias and Preference Acquisition
ACM Transactions on Interactive Intelligent Systems (TiiS)
Comer, Comentar e Compartilhar: Análise de uma Rede de Ingredientes e Receitas
Proceedings of the X Brazilian Symposium in Collaborative Systems
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With the number of people considered to be obese rising across the globe, the role of IT solutions in health management has been receiving increased attention by medical professionals in recent years This paper focuses on an initial step toward understanding the applicability of recommender techniques in the food and diet domain By understanding the food preferences and assisting users to plan a healthy and appealing meal, we aim to reduce the effort required of users to change their diet As an initial feasibility study, we evaluate the performance of collaborative filtering, content-based and hybrid recommender algorithms on a dataset of 43,000 ratings from 512 users We report on the accuracy and coverage of the algorithms and show that a content-based approach with a simple mechanism that breaks down recipe ratings into ingredient ratings performs best overall.