Enhancing Personalized Recipe Recommendation through Multi-Class Classification
Enhancing Personalized Recipe Recommendation through Multi-Class Classification
Harish Neelam and Koushik Sai Veerella, Michigan State University, USA
Abstract
This paper intends to address the challenge of personalized recipe recommendation in the realm of diverse culinary preferences. The problem domain involves recipe recommendations, utilizing techniques such as association analysis and classification. Association analysis explores the relationships and connections between different ingredients to enhance the user experience. Meanwhile, the classification aspect involves categorizing recipes based on user-defined ingredients and preferences. A unique aspect of the paper is the consideration of recipes and ingredients belonging to multiple classes, recognizing the complexity of culinary combinations. This necessitates a sophisticated approach to classification and recommendation, ensuring the system accommodates the nature of recipe categorization. The paper seeks not only to recommend recipes but also to explore the process involved in achieving accurate and personalized recommendations.
Keywords
Data Mining, Ingredients, Association rules, Classification, Recommendations, Recipes, Apriori, FP Growth, Networks, Similarity Scores, Filtering
Full Text : https://aircconline.com/ijcseit/V14N5...
Volume URL : https://airccse.org/journal/ijcseit/v...
#datamining #ingredients #classification #recommendations #receipes #networks #filtering #artificialintelligence