Deep Learning-Based Neural Network Embedding for Book Recommendation Systems

Abstract

Recommender Systems are a type of artificial intelligence that uses machine learning algorithms to analyze user data and provide personalized recommendations. These systems have been increasingly popular in recent years and have had a significant impact on various industries, including e-commerce, entertainment, social media, and healthcare. One of the key benefits of Recommender Systems is their ability to replicate the decision-making abilities of human experts. By analyzing large amounts of data, these systems can identify patterns and make predictions about what users are likely to be interested in. This can lead to improved decision-making and more efficient processes in a variety of domains. In the context of Book Recommender Systems (BRS), these systems can help librarians manage their catalogs more efficiently by suggesting books that are relevant to the library’s collection and the interests of its readers. This can help improve user satisfaction and promote the discovery of new books and authors. BRS can also support readers in choosing the best book for them by taking into account their reading history and preferences. In the e-commerce industry, merchants can use BRS to manage their inventory and increase profits. By recommending products that are likely to be of interest to customers, based on their purchase history and browsing behavior, BRS can help merchants increase sales and improve customer satisfaction.