Food Recommendation using Ontology and Heuristics
This work addresses food personalization for users seeking healthier options, but it is incremental as it builds on existing frameworks and methods.
The authors tackled the problem of food recommendation by proposing a semantic framework that combines TF-IDF term extraction with cosine similarity, incorporating healthy heuristics and a food database. They concluded that semantic recommender systems outperform traditional ones in accuracy, precision, and recall, with their method achieving a better F-measure than existing semantic recommenders.
Recommender systems are needed to find food items of ones interest. We review recommender systems and recommendation methods. We propose a food personalization framework based on adaptive hypermedia. We extend Hermes framework with food recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.