%0 Conference Paper %1 10.1007/978-3-642-13470-8_36 %A Freyne, Jill %A Berkovsky, Shlomo %B User Modeling, Adaptation, and Personalization %C Berlin, Heidelberg %D 2010 %E De Bra, Paul %E Kobsa, Alfred %E Chin, David %I Springer Berlin Heidelberg %K food-recommender umap2010 %P 381--386 %T Recommending Food: Reasoning on Recipes and Ingredients %X 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. %@ 978-3-642-13470-8

@inproceedings{10.1007/978-3-642-13470-8_36, abstract = {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.}, added-at = {2021-08-13T17:41:08.000+0200}, address = {Berlin, Heidelberg}, author = {Freyne, Jill and Berkovsky, Shlomo}, biburl = {https://www.bibsonomy.org/bibtex/2439af4fc9ea176ddeb4fc31caf051133/brusilovsky}, booktitle = {User Modeling, Adaptation, and Personalization}, description = {Recommending Food: Reasoning on Recipes and Ingredients | SpringerLink}, editor = {De Bra, Paul and Kobsa, Alfred and Chin, David}, interhash = {cd4011a3ffc92189de0ccbf9c43dc4b4}, intrahash = {439af4fc9ea176ddeb4fc31caf051133}, isbn = {978-3-642-13470-8}, keywords = {food-recommender umap2010}, pages = {381--386}, publisher = {Springer Berlin Heidelberg}, timestamp = {2021-08-13T17:41:08.000+0200}, title = {Recommending Food: Reasoning on Recipes and Ingredients}, year = 2010 }