Enhanced Book Recommender System -A Content based Approach.

People require some tools to seek and collect useful information because it is difficult in this information era to sift through the large amount of material that is available on internet platforms. One of these tools is referred to as a recommendation system, which is a potent software method that assists in quickly traversing through large volumes of data to determine user's interests and give the necessary information. In this paper the three approaches for a content-based book recommendation system are discussed. In the first strategy, a simple system for recommending books is created, with certain input criteria like author, publisher, language, average rating, and recommendations for the books as the output. In the second method the K nearest neighbor (KNN) algorithm and cosine similarity are used to develop a book recommendation system based on several attributes. In the third method, the user enters a book title, which is then translated into vectors using Term Frequency-Inverse Document Frequency (TF-IDF), which then calculates the cosine similarity between related books before proposing related and similar books to the user. By implementing the above strategies, the user will be able to view book recommendations based on various attributes.