Abstract
The growth and popularity of streaming music have changed the way people consume music, and users can listen to online music anytime and anywhere. By integrating various recommendation algorithms/strategies (user profiling, collaborative filtering, content filtering, etc.), we capture users' interests and preferences and recommend the content of interest to them. To address the sparsity of behavioral data in digital music marketing, which leads to inadequate mining of user music preference features, a metric ranking learning recommendation algorithm with fused content representation is proposed. Relative partial order relations are constructed using observed and unobserved behavioral data to enable the model to be fully trained, while audio feature extraction submodels related to the recommendation task are constructed to further alleviate the data sparsity problem, and finally, the preference relationships between users and songs are mined through metric learning. Convolutional neural networks are used to extract the high-level semantic features of songs, and then the high-level semantic features of songs extracted from the previous layer are reformed into a session time sequence list according to the time sequence of user listening in order to build a bidirectional recurrent neural network model based on the attention mechanism so that it can reduce the influence of noisy data and learn the strong dependencies between songs.
Highlights
In the middle of the development of the Internet, these traditional, crude online marketing methods are no longer adapted to the current marketing scene
In order to mitigate the impact of noisy data on song recommendation quality, the current industry approach is to identify the contribution of different songs to the target song mainly by introducing an “attention mechanism” to reduce the interference caused by noisy data; most models only stay at the global level of attention mechanism research but do not consider the finegrained level. e impact of local noise of songs makes the models only good at handling recommendation scenarios with long session data, and the recommendation quality drops sharply when facing short session data
In the era of the Internet digital economy, the marketing and promotion of digital content rely more on online marketing theory, but only a few studies have focused on digital music, especially the marketing methods based on personalized recommendations [13]. e paper more systematically studies the personalized marketing methods of digital music, which can expand the scope of the application of online marketing theory in the field of digital music and promote the development concept of online marketing to personalized marketing. e music recommendation system is a huge system engineering; its core work mainly contains feature engineering, algorithm research, engineering implementation, component development, etc
Summary
International Cultural Exchange Center, HanDan University, Handan City, Hebei Province 056005, China. Recommendation algorithms that fuse content features rely on song metadata information and user description information to construct song representations and user representations and compute the similarity between user representations and song representations to recommend songs that sound similar to J or have similar semantics to users Since this type of approach does not require modeling the user’s behavior, it can effectively alleviate the cold-start music recommendation problem. With the successful application of deep learning technology in the fields of image recognition and natural language processing, researchers have started to introduce deep neural networks to extract the high-level semantic features of audio content and produce recommendations by organically integrating them with traditional recommendation algorithms.
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