Abstract:
Intelligent algorithms have grown to become the backbone of recommendation systems. These algorithms are able to offer results for users in the form of suggestions. They lessen the workload that is associated with selecting the finest options from among the many available. These days, Recommender systems may be deployed in any field, from online shopping to network security, in the form of customised service options.
They are beneficial to both the customer and the producer by making suggestions to consumers regarding things that the consumers cannot demand until the recommendations have been made. User and item are the two components that make up every recommender system. The first is the user, and the second is the item. A client or consumer of any product or products might be considered a user because they are the ones that get the ideas.
A database of users and goods can serve as an input to a recommendation algorithm, and the suggestions themselves will, of course, serve as the output. The data about the consumers and the books serve as input for this system, and the result of this book is a list of book suggestions.
Within the scope of this study, a novel methodology for advising purchasers on book selection is presented. In order to provide effective suggestions, this system employs not only content filtering but also collaborative filtering and association rule mining.
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