With the rapid development of the Internet, the number of digital music has increased dramatically. It is very difficult for users to dig out the songs they are interested in from the massive digital music information, and the problem of "information overload" has emerged. A search engine is a tool for retrieving information. When a user submits a query to the search engine, the relevant content is returned to the user after retrieval. The search results of different search engines are different. Through the analysis of users' behaviors, we can understand users' search habits and realize a search engine that is more in line with users' needs. In order to accurately recommend interesting music to users and improve the market share of application objects, this paper designs a music intelligent recommendation system based on user interest preference, which is composed of data acquisition module, offline data processing module and online recommendation module. The data acquisition module collects user registration information, online scoring data of target music, and conducts online survey to collect relevant data. Data analysis and experiments show that the model is superior to the comparison model in terms of training efficiency and accuracy of recommendation, thus verifying the effectiveness of integrating user's long-term and short-term preferences and user influence for recommendation.
One of the main purposes of music recommendation system is how to recommend the songs that users expect from the massive song data. Most people will use the search function of the software to search for some singers or favorite song categories they have known before. However, the search results do not consider that users are different individuals and have different preferences for songs, which leads to low user satisfaction. Driven by big data, this article proposes a individuation recommendation algorithm for pop music based on deep learning. At present, the music resources on the Internet are extremely rich, and users of various music platforms are facing the troubles of too many kinds of music and difficult to express their emotions while enjoying the leisure time brought by music. By analyzing the music files in the system and the massive user behavior records saved, the user's interest preferences are obtained, and personalized music service content is provided to users. The simulation results show that the individuation recommendation algorithm of pop music in this article is better than the traditional Collaborative Filtering (CF) in recommendation accuracy and user rating.
With the continuous improvement of public aesthetics and more and more music with different changes and styles, the music retrieval system should be more efficient and diversified. However, the traditional music classification system often needs enough perfect music samples at the initial stage of training, and it cannot be effectively adjusted with the addition of various new music samples. At present, most audio music classification algorithms include two stages: feature extraction stage and classification stage. Many musical features can be used to realize this algorithm, including short-time energy and short-time zero-crossing rate in time domain, bandwidth and spectral centroid in frequency domain, and MFCC coefficient based on auditory perception. The task of music style classification is to classify the music into a certain style by processing the data of music signals. Using the music style classification system can help users quickly find music of relevant styles and achieve more effective management of music database. In this paper, Support Vector Machine (SVM) algorithm is used to classify UCI standard data sets. The results show that the learning function with simple structure is adopted for the data set with few training samples. For the dataset with more training samples, the learning function with simple structure will reduce the generalization ability of machine learning.
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