A Hybrid Music Recommendation Algorithm Based on Attention Mechanism

2021 ◽  
pp. 328-339
Author(s):  
Weite Feng ◽  
Tong Li ◽  
Haiyang Yu ◽  
Zhen Yang
Keyword(s):  
Attention Mechanism ◽  
Music Recommendation ◽  
Recommendation Algorithm

scholarly journals Visualization of the Artist Relations Using Twitter User Profiles

2020 ◽  
Author(s):  
Minoru Yoshida ◽  
Shogo Kohno ◽  
Kazuyuki Matsumoto ◽  
Kenji Kita
Keyword(s):  
User Profile ◽  
Word Embedding ◽  
New Music ◽  
User Profiles ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Twitter User ◽  
Streaming Services ◽  
Music Streaming

We propose a new music artist recommendation algorithm using Twitter profile texts. Today, music recommendation is provided in many music streaming services. In this paper, we propose a new recommendation algorithm for this music recommendation task. Our idea is to use Twitter profile texts to find appropriate artist names to recommend. We obtained word embedding vectors for each artist name by applying word2vec algorithm to the corpus obtained by collecting such user profile texts, resulting in vectors that reflect artist co-occurrence in the profile texts.

scholarly journals Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hui Ning ◽  
Qian Li
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Contextual Information ◽  
Rapid Development ◽  
Theoretical Research ◽  
Context Information ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Points Of Interest ◽  
Popularity Prediction

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.

scholarly journals Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuanyuan Zhang
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Big Data ◽  
Convolutional Neural Network ◽  
Recommendation System ◽  
Piano Music ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Data Environment ◽  
Music Recommendation System

In the era of big data, the problem of information overload is becoming more and more obvious. A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music recommendation for users in the big data environment. The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on. In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences. Then, music that users may be interested in is generated and sorted in order to recommend new piano music to relevant users. The image analysis model contains four “convolution + pooling” layers. The classification accuracy and gradient change law of the CNN under RMSProp and Adam optimal controllers are compared. The image analysis results show that the Adam optimal controller can quickly find the direction, and the gradient decreases greatly. In addition, the accuracy of the recommendation system is 55.84%. Compared with the traditional CNN algorithm, this paper uses the convolutional neural network (CNN) to analyze and recommend piano music images according to users’ preferences, which can realize more accurate piano music recommendation for users in the big data environment. Therefore, the piano music recommendation system based on the CNN has strong feature learning ability and good prediction and recommendation ability.

scholarly journals Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

2021 ◽  
Author(s):  
Zhisheng Yang ◽  
Jinyong Cheng
Keyword(s):  
Deep Learning ◽  
Auxiliary Information ◽  
Substantial Improvement ◽  
User Preference ◽  
Knowledge Graph ◽  
Historical Records ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Recommendation Algorithms

Abstract In recommendation algorithms, data sparsity and cold start problems are always inevitable. In order to solve such problems, researchers apply auxiliary information to recommendation algorithms to mine and obtain more potential information through users' historical records and then improve recommendation performance. This paper proposes a model ST_RippleNet, which combines knowledge graph with deep learning. In this model, users' potential interests are mined in the knowledge graph to stimulate the propagation of users' preferences on the set of knowledge entities. In the propagation of preferences, we adopt a triple-based multi-layer attention mechanism, and the distribution of users' preferences for candidate items formed by users' historical click information is used to predict the final click probability. In ST_RippleNet model, music data set is added to the original movie and book data set, and the improved loss function is applied to the model, which is optimized by RMSProp optimizer. Finally, tanh function is added to predict click probability to improve recommendation performance. Compared with the current mainstream recommendation methods, ST_RippleNet recommendation algorithm has very good performance in AUC and ACC, and has substantial improvement in movie, book and music recommendation.

scholarly journals A music recommendation algorithm based on clustering and latent factor model

2020 ◽  
Vol 309 ◽  
pp. 03009
Author(s):  
Yingjie Jin ◽  
Chunyan Han
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
User Preference ◽  
Implicit Feedback ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Latent Factor ◽  
Latent Factor Models ◽  
Feedback Data ◽  
Preference Matrix

The collaborative filtering recommendation algorithm is a technique for predicting items that a user may be interested in based on user history preferences. In the recommendation process of music data, it is often difficult to score music and the display score data for music is less, resulting in data sparseness. Meanwhile, implicit feedback data is more widely distributed than display score data, and relatively easy to collect, but implicit feedback data training efficiency is relatively low, usually lacking negative feedback. In order to effectively solve the above problems, we propose a music recommendation algorithm combining clustering and latent factor models. First, the user-music play record data is processed to generate a user-music matrix. The data is then analyzed using a latent factor probability model on the resulting matrix to obtain a user preference matrix U and a musical feature matrix V. On this basis, we use two K- means algorithms to perform user clustering and music clustering on two matrices. Finally, for the user preference matrix and the commodity feature matrix that complete the clustering, a user-based collaborative filtering algorithm is used for prediction. The experimental results show that the algorithm can reduce the running cost of large-scale data and improve the recommendation effect.

scholarly journals Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Jianrui Chen ◽  
Zhihui Wang ◽  
Tingting Zhu ◽  
Fernando E. Rosas
Keyword(s):  
Collaborative Filtering ◽  
Double Layer ◽  
State Of The Art ◽  
Single Layer ◽  
Attention Mechanism ◽  
User Preference ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
The Status ◽  
Single Layer Network

The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

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