The importance of nutrition has been gaining priority in an increasingly health-conscious world. This creates an increased demand for nutritional information through which people can make informed decisions about their dietary choices and consequently align them with their nutritional goals.
Real Time Food Searching & RecommendationWeb App
Industry: Food
Region: USA
SERCVICE USED
- Team Extension
- Web Development
- Mobile Deelopment
- UX/UI Design
Technology Stack
Introduction
Challenge
There is a need to match restaurant food items from different states within the US to their respective recipes and find out their nutritional values in order to recommend foods to users based on their respective nutritional plans and history.
Approach
Keeping in mind restaurants use different names for the same recipe, our initial focus was to find similarities between restaurant menu items with millions of recipe names in real life. After cleaning and normalizing data, Word2vec model was trained to find out the numerical representations of recipes and menu items. For searching purposes, we utilized the N-Gram model till bi-gram. Since working with huge sets of data was time-consuming, Locality Sensitive hashing techniques were used to speed up the process multiple times.
The recommendation system was built in order to recommend food items to users based on their nutritional map and history. This consisted of a hybrid variant of collaborative and content filtering. Graph databases give the ability to connect multiple records together and explore the consequent relationships among them allowing real-time, transaction-level analysis of customer behavior to become efficient. Stochastic and Neural Network-based matrix factorization techniques were employed for collaborative filtering to achieve better results. Different similarity functions were used to recommend similar foods to users based on foods they had already liked.
Outcome
Our two-phase solution-based implementation of the project has enabled access to provide recommendations for nutritional values of food being offered across restaurants in the U.S. This will allow health-conscious decisions among users in terms of setting and achieving nutritional goals. It also allows users to improve eating habits which will boost energy levels throughout the day ultimately boosting productivity, efficiency and paving the way for a healthier future.
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Real Time Food Searching & Recommendation
Matching restaurant food items to their respective recipes to gauge nutritional values to users.
Industry: Food
Region: USA
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Braden Ericson | Founder & CEO Sparrow marketing
Datics team was great to work with. They were very proactive and helped me define the requirements for the project and were able to navigate the pivots I threw at them during our development period. They built a database, APIs, and an ML model beyond expectations. The team is actively fixing bugs that are popping up as we test and are a fun team to have weekly syncs with.
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