Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph

• URL: http://arxiv.org/abs/2101.01775v1
• Date: Tue, 5 Jan 2021 20:38:16 GMT
• Title: Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph
• Authors: Yu Chen, Ananya Subburathinam, Ching-Hua Chen and Mohammed J. Zaki
• Abstract summary: We propose a novel problem formulation for food recommendation, modeling this task as constrained question answering over a large-scale food knowledge base/graph (KBQA) Besides the requirements from the user query, personalized requirements from the user's dietary preferences and health guidelines are handled in a unified way. Our approach significantly outperforms non-personalized counterparts.
• Score: 16.58534326000209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Food recommendation has become an important means to help guide users to adopt healthy dietary habits. Previous works on food recommendation either i) fail to consider users' explicit requirements, ii) ignore crucial health factors (e.g., allergies and nutrition needs), or iii) do not utilize the rich food knowledge for recommending healthy recipes. To address these limitations, we propose a novel problem formulation for food recommendation, modeling this task as constrained question answering over a large-scale food knowledge base/graph (KBQA). Besides the requirements from the user query, personalized requirements from the user's dietary preferences and health guidelines are handled in a unified way as additional constraints to the QA system. To validate this idea, we create a QA style dataset for personalized food recommendation based on a large-scale food knowledge graph and health guidelines. Furthermore, we propose a KBQA-based personalized food recommendation framework which is equipped with novel techniques for handling negations and numerical comparisons in the queries. Experimental results on the benchmark show that our approach significantly outperforms non-personalized counterparts (average 59.7% absolute improvement across various evaluation metrics), and is able to recommend more relevant and healthier recipes.

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