SIRR: Semantically Infused Recipe Recommendation Model Using Ontology Focused Machine Intelligence
SIRR: Semantically Infused Recipe Recommendation Model Using Ontology Focused Machine Intelligence
- New search for: Anand, Mrinal
- New search for: Deepak, Gerard
- New search for: Santhanavijayan, A.
- New search for: Sharma, Sanjay
- New search for: Subudhi, Bidyadhar
- New search for: Sahu, Umesh Kumar
- New search for: Anand, Mrinal
- New search for: Deepak, Gerard
- New search for: Santhanavijayan, A.
In the current era, recipe recommendation is of extreme significance as there is no expert intelligence system specifically for recommendation. In the era of Web 3.0, semantically driven frameworks are mostly absent. As a result, semantically infused recipe recommendation model is highly important. This study proposes a semantically infused intelligent approach to recommend recipes using Ontology Focused Machine Intelligence to classify the documents. The semantic similaritySemantic Similarity was computed by hybridizing Normalized Compression Distance (NCDNormalized Compression Distance (NCD)) and KL divergenceKL Divergence under a cultural algorithmCultural Algorithm. The model uses both Recurrent Neural NetworkRecurrent Neural Network (RNN) and XGBoostXGBoost at different locations to sort the data and compute semantic similaritySemantic Similarity. The model yielded an FDR of 0.04, and an accuracy of 97.09%.
Document information
SIRR: Semantically Infused Recipe Recommendation Model Using Ontology Focused Machine Intelligence
Lect. Notes Electrical Eng.
International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022
2023-11-18
12 pages
Article/Chapter (Book)
Electronic Resource
English
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