Abstract
In the era of Industry 4.0 and 5G, various dance music websites provide thousands of dances and songs, which meet people's needs for dance music and bring great convenience to people. However, the rapid development of dance music has caused the overload of dance music information. Faced with a large number of dances and songs, it is difficult for people to quickly find dance music that conforms to their own interests. The emergence of dance music recommendation system can recommend dance music that users may like and help users quickly discover or find their favorite dances and songs. This kind of recommendation service can provide users with a good experience and bring commercial benefits, so the field of dance music recommendation has become the research direction of industry and scholars. According to different groups of individual aesthetic standards of dance music, this paper introduces the idea of relation learning into dance music recommendation system and applies the relation model to dance music recommendation. In the experiment, the accuracy and recall rate are used to verify the effectiveness of the model in the direction of dance music recommendation.
Highlights
At present, human society has entered the 5G era and Industry 4.0
On the basis of literature research, this study proposes that dance music recommendation system includes 10 dimensions: content, functionality, page design, response speed, perceived usability, perceived usefulness, satisfaction, confidence, perceived experience, and potential risk. e interview results show that due to the upgrade of media equipment and the improvement of network speed, the dimension of “response speed” is of little significance and should be deleted
Referring to the correlation model of image classification, this paper introduces the model of Relationship-Learning into the field of music recommendation
Summary
Human society has entered the 5G era and Industry 4.0. With the continuous development of big data, artificial intelligence, and other technologies, the amount of data will expand rapidly, and the phenomenon of data overload will become more significant. e 46th Statistical Report on Internet Development in China released by China Internet Network Information Center shows that the total number of Internet users in China will reach 940 million in 2020. 2. Individualized Analysis and Recommendation Technology of Dance Music e recommendation engine uses different algorithms to filter data and recommend the most relevant items to users. According to the qualitative research results, this paper generated the evaluation scale of dance music recommendation system, which consists of 8 dimensions and 30 items: content, functionality, page design, perceived ease of use, perceived usefulness, confidence, perceived experience, and potential risk. Collaborative filtering algorithm can recommend various favorite contents to users according to historical data, but the recommendation results may not be satisfactory when the scoring data are sparse, large-scale matrix operation may be needed when the data volume is huge, and the time consumption will increase linearly with the data scale, so real-time recommendation cannot be achieved. E TF-IDF algorithm is used to calculate the words with greater weight in the data, and the cosine similarity algorithm mentioned above is used to find other songs similar to this song and recommend them to users
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