Abstract: Various health issues and diseases are caused due to inadequate and inappropriate intake of food. Due to degradation in the concise information about healthy diet, people have to rely on medicines instead of taking preventive measures in food intake. As there is large diversity in food components and variety of dietary sources available, it makes it more challenging to perform real-time selection of diet patterns that must fulfill one’s nutrition needs. Particularly, selection of proper diet is critical for patients suffering from various diseases. In this paper, we highlight the issue of selection of proper diet that must fulfill nutrition requirements of an individual. To address this issue, we present a content based food recommendation system, for dietary recommendations based on users inputs and their lifestyle. The model uses Naïve Bayes algorithm to generate optimal food list and recommends suitable foods according to the diversity in inputs of users. Diet Recommendation System can play a vital role in controlling various diseases. The experimental results show that compared to single node execution, the convergence time of parallel execution on cloud is approximately 12 times lower. Moreover, adequate accuracy is attainable by increasing the number of inputs to system.
Keywords: Information Retrieval, Recommendation Systems, Content- Based Approach, Expert Systems, Web Crawling, Feature Similarity, Naïve Bayes.
Title: Web portal for Diet Recommendation System
Author: Amol R. Dhakne, Shahbaz Q. Khan, Jasvindersingh T. Kohli, Shubham R. Salunke, Adhvaryu A. Fulzele
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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