Abstract: Artificial intelligence (AI) can be easily implemented in e-commerce and brought positive changes to the way that users interact with online platforms as a basis for shopping. This project involves the implementation of an industry-ready, deep learning-based recommendation system developed for e-commerce grocery applications with both content-based and collaborative filtering incorporated in a web-app framework written in Flask. The system uses content-based filtering by taking into consideration the metadata of the product, which are the description, category, nutritional information, etc., and users’ buying history, to provide recommendations in harmony with the users’ preferences. At the same time, collaborative filtering identifies patterns for the entire user base and uses additional methods, including matrix factorization and k-nearest neighbors that find the similarities between users and items, increase the range of recommendations and their relevance. For optimal usability, the recommendation system is embedded in a Flask web application, which offers a practical and hierarchical interface where the user can navigate through the grocery products list, state the preferences, and get the recommendations. The features of implementation include technical support for practical database, real-time computation of recommendations and actual scalability for the large amount of data. This sub-discipline deals with performance using algorithms, with help of Python tools – Scikit-learn and Pandas —logarithm for analyzing the data and accuracy is measured with the help of such basic metrics as precision, recall and RMSE. This integration approach links the concepts of AI and web development to redefine the conversation of grocery shopping from LSTM ensuring the users get accurate, timely and attractive product suggestions as a means of boosting the convenience and satisfaction.
Keywords: e-commerce grocery, content-based filtering, collaborative filtering, Flask web application, personalized suggestions, machine learning, user behavior analysis.
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DOI:
10.17148/IJARCCE.2025.14696
