Deep Learning and Data Mining for Book Recommendation: Retrospect and Expectation
Qiyuan Fu, Jinmin Fu, Disheng Wang · 2022
With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines, and recommendation system have become the most convenient ways to find relevant information within a short time. The technique of Book Recommendation System (BRS) combines machine learning, data mining, and artificial intelligence, which is regarded as an expert to offer advice according to personal related choices and decisions before. The recommendation system is one of the strongest tools to increase profit and retain buyers. Book Recommendation System finds useful patterns in rating and consumption data between readers and books, then exploits these patterns to guide users to good books. Many of these patterns reflect subsistent phenomena between the various users and books. But there remain some unsolved questions, such as offering advice to persons who are firstly using the system, recommending books when avoiding some taboos like religious taboos. In this paper, I proposed a retrospective study of BRS and also made an expectation of the future development of the domain.