Design of Intelligent Recommendation Algorithm for Music Website Based on Deep Learning and Artificial Intelligence

As an important way of communication and expression, music has become a part of people’s daily activities. Judging from the growth of the music market, the application of digital multimedia technology is more extensive, and many music industries have begun to pay attention to online music services. The introduction of data mining (DM) in the construction of music websites can effectively solve the problem of how users choose their favorite music in the face of a large number of music provided by websites. In this article, an intelligent recommendation algorithm for music websites based on deep learning is proposed. Based on the traditional collaborative filtering (CF) recommendation, situational information is integrated, and the problem of data sparsity is solved by implicit feedback score conversion. The results show that the recommendation algorithm with situational information is better than the traditional recommendation algorithm, which has certain reference significance for optimizing the music recommendation system. The recommendation engine in the intelligent recommendation system actively recommends the music that users may like, which can change the mode of users actively searching for music, increase the number of visits to music websites and bring commercial benefits to music websites.