Semantic Modeling for Food Recommendation Explanations

• URL: http://arxiv.org/abs/2105.01269v1
• Date: Tue, 4 May 2021 03:25:36 GMT
• Title: Semantic Modeling for Food Recommendation Explanations
• Authors: Ishita Padhiar, Oshani Seneviratne, Shruthi Chari, Daniel Gruen, Deborah L. McGuinness
• Abstract summary: We present the Food Explanation Ontology (FEO) that provides a formalism for modeling explanations to users for food-related recommendations. FEO uses a modular structure that lends itself to a variety of explanations while still preserving important semantic details. Our motivation with the use of FEO is to empower users to make decisions about their health, fully equipped with an understanding of the AI recommender systems as they relate to user questions.
• Score: 0.5833117322405447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increased use of AI methods to provide recommendations in the health, specifically in the food dietary recommendation space, there is also an increased need for explainability of those recommendations. Such explanations would benefit users of recommendation systems by empowering them with justifications for following the system's suggestions. We present the Food Explanation Ontology (FEO) that provides a formalism for modeling explanations to users for food-related recommendations. FEO models food recommendations, using concepts from the explanation domain to create responses to user questions about food recommendations they receive from AI systems such as personalized knowledge base question answering systems. FEO uses a modular, extensible structure that lends itself to a variety of explanations while still preserving important semantic details to accurately represent explanations of food recommendations. In order to evaluate this system, we used a set of competency questions derived from explanation types present in literature that are relevant to food recommendations. Our motivation with the use of FEO is to empower users to make decisions about their health, fully equipped with an understanding of the AI recommender systems as they relate to user questions, by providing reasoning behind their recommendations in the form of explanations.

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