Abstract:
Intelligent algorithms generate user recommendations in recommendation systems. They reduce decision-making overhead. Personalized recommender systems can now be used in E-commerce and network security.
They benefit consumers and manufacturers by suggesting items that can’t be demanded until the recommendations. User and item make up every recommender system. Any product or service user can receive suggestions.
Recommendation algorithms take user and item databases as input and output recommendations. This system takes customer and book data and outputs book recommendations. This paper recommends books to buyers in a new way. Content filtering, collaborative filtering, and association rule mining make these recommendations efficient.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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