Abstract
With the advent of the era of big data, the rise of Web2.0 completely subverts the traditional Internet model and becomes the trend of today's information age. Simultaneously, massive amounts of data and information have infiltrated various Internet companies, resulting in an increase in the problem of information overload. In the online world, learning how to quickly and accurately select the parts we are interested in from a variety of data has become a hot topic. Intelligent music recommendation has become a current research hotspot in music services as a viable solution to the problem of information overload in the digital music field. On the basis of precedents, this paper examines the characteristics of music in a comprehensive and detailed manner. A knowledge graph-based intelligent recommendation algorithm for contemporary popular music is proposed. User-defined tags are described as the free genes of music in this paper, making it easier to analyze user behavior and tap into user interests. It has been confirmed that this algorithm's recommendation quality is relatively high, and it offers a new development path for improving the speed of searching for health information services.
Highlights
Intelligent music recommendation has become a research hotspot in current music services [3] as an effective way to solve the problem of information overload in the field of digital music
There are two main types of situational user preference extraction technologies: quantitative analysis and qualitative analysis. e node type and relationship type in the knowledge map contain the design idea of the ontology library, and the entity information and semantic information between entities in the map show the extracted movie knowledge. e constructed knowledge map nodes and relationship types are reasonably designed, and the knowledge is accurate and comprehensive, which can be used for pop music retrieval
As a new application in the field of recommendation systems, intelligent music recommendation has a lot of research potential
Summary
Recommendation system is a smart software technology that can provide personalized recommendation services for users based on their interests, preferences, and characteristics [1]. e amount of music available is growing at an exponential rate, making it difficult for users to search for and find related music. Recommendation system is a smart software technology that can provide personalized recommendation services for users based on their interests, preferences, and characteristics [1]. Intelligent music recommendation has become a research hotspot in current music services [3] as an effective way to solve the problem of information overload in the field of digital music. What users need is to be able to play music that suits their interests continuously. How to provide different users with a list of songs that suit their interests or recommend interesting songs for them has become a problem that the current music recommendation system needs to solve. Intelligent music recommendation system is faced with the problems of accuracy, diversity, and novelty of recommendation, while its data set is sparse and information is missing
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