Abstract
In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user's preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.
Highlights
Introduction e rapid development of theInternet has brought a large amount of information to people, meeting their needs for information in the information age and benefiting them from it, but it has brought the problem of information overload, and both consumers and producers of information have encountered great challenges
We study the recommendation algorithm that fits the business scenario, how the recommendation engine works. e optimization direction of the algorithm is given in conjunction with the actual problem [5]. e idea of solving the problem of cold start and user interest prediction in the music recommendation scenario is given, which is a guideline for the implementation in recommendation system engineering applications. e recommendation system can reach the demand of a personalized selection of users, help e-commerce enterprises to better serve users, improve competitiveness and achieve profitability
How to distinguish the differences in music preferences among users is the key to improving marketing efficiency, for which a recommendation algorithm based on multilayer attention representation is proposed. e algorithm uses information such as user attributes and song content to learn the embedded representations of songs from a multidimensional perspective and to mine the preference relationships between users and songs
Summary
Internet has brought a large amount of information to people, meeting their needs for information in the information age and benefiting them from it, but it has brought the problem of information overload, and both consumers and producers of information have encountered great challenges. Faced with the huge amount of information, as consumers of information, people cannot get the information that is useful to them, the efficiency of using information is reduced instead, and a large amount of invalid information plagues human daily life; as producers of information, it becomes very difficult to make the information they produce stand out and be noticed by most users [1]. In response to the growing problem of information overload, search engines and recommendation systems have emerged as two complementary tools. With the continuous development and application of digital multimedia technology, increased music industries are turning to online music services, but as Computational Intelligence and Neuroscience music libraries become larger and larger and music resources become increasingly abundant [3]
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