The Recipe Recommender
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Basics: Recipe Recommender - My Bachelor Thesis
My bachelor thesis investigated the effectiveness of algorithms for personalized recipe recommendations. The research tested whether collaborative filtering or content-based approaches yield more satisfying results in terms of matching users’ taste. To achieve this, a software application was developed, implementing both ItemKNN and content-based algorithm, allowing for a direct comparison of their recommendation outcomes.Learn more
Short facts
2 Algorithms
Two different recommendation algorithms, to directly compare them against each other. Collaborative filtering (ItemKNN) and content-based.
Over 1000 recipes
A database with over 1000 diverse recipes was used, created with the Spoonacular Food API.
Great user experience
Despite having the main purpose of supporting my research, the Recipe Recommender offered a great user experience and might in the future be applied in real-word and public projects offering personalized recipe recommendations
Results
ItemKNN / Collaborative Filtering
Analyzing the results revealed a statistically significant difference in user ratings, with the ItemKNN algorithm receiving higher average ratings (4.2 out of 5 stars) compared to the content-based algorithm (3.389 stars). This suggests that, on average, users found the recommendations provided by the ItemKNN algorithm more appealing and satisfying. The study thus underlines the effectiveness of collaborative filtering approaches in the context of personalized recipe recommendations.
Software architecture
The architecture of the Recipe Recommender system was documented with parts of the arc42 software architecture documentation template. I used the arc42 Architecture Communication Canvas to give a brief overview of the software architecture.The canvas for the Recipe Recommender can be found below.
