Music recommendation algorithm based on user portrait

Li, Jiaxin

doi:10.1109/cei52496.2021.9574460

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Using statistical analysis, clustering and other methods to label users, a user portrait system based on text mining is designed. Reference [12] quantifies, analyzes and processes data information through related algorithms and models, and proposes an improved music recommendation algorithm, which enriches the application of user portraits in the field of music recommendation. Reference [13] constructed an electric vehicle user portrait index system from three dimensions: electric response potential, time response potential and spatial response potential.…”

Section: Related Workmentioning

confidence: 99%

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

Dong¹,

Liang²,

Yu³

et al. 2023

Computers, Materials &Amp; Continua

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With the frequent occurrence of telecommunications and network fraud crimes in recent years, new frauds have emerged one after another which has caused huge losses to the people. However, due to the lack of an effective preventive mechanism, the police are often in a passive position. Using technologies such as web crawlers, feature engineering, deep learning, and artificial intelligence, this paper proposes a user portrait fraud warning scheme based on Weibo public data. First, we perform preliminary screening and cleaning based on the keyword "defrauded" to obtain valid fraudulent user Identity Documents (IDs). The basic information and account information of these users is user-labeled to achieve the purpose of distinguishing the types of fraud. Secondly, through feature engineering technologies such as avatar recognition, Artificial Intelligence (AI) sentiment analysis, data screening, and follower blogger type analysis, these pictures and texts will be abstracted into user preferences and personality characteristics which integrate multidimensional information to build user portraits. Third, deep neural network training is performed on the cube. 80% percent of the data is predicted based on the N-way K-shot problem and used to train the model, and the remaining 20% is used for model accuracy evaluation. Experiments have shown that Few-short learning has higher accuracy compared with Long Short Term Memory (LSTM), Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN). On this basis, this paper develops a WeChat small program for early warning of telecommunications network fraud based on user portraits. When the user enters some personal information on the front end, the back-end database can perform correlation analysis by itself, so as to match the most likely fraud types and give relevant early warning information. The fraud warning model is highly scaleable. The data of other Applications (APPs) can be extended to further improve the efficiency of anti-fraud which has extremely high public welfare value.

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Section: Related Workmentioning

confidence: 99%

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

Dong¹,

Liang²,

Yu³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In the past decades, a multitude of recommendation algorithms has been developed [3], [4]. However, there are still large amounts of challenges and dilemmas existed in RSs, such as unsatisfying recommendation accuracy, and real-time recommendation problems.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Music Recommendation Model Based on Personalized Measurement and Game Theory

Yun,

Jian,

Yanlong

2023

Chinese J. Elect.

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Music recommendation algorithms, from the perspective of real-time, can be classified into two categories: offline recommendation algorithms and online recommendation algorithms. To improve music recommendation accuracy, especially for the new music (users have no historic listening records on it), and real-time recommendation ability, and solve the interest drift problem simultaneously, we propose a hybrid music recommendation model based on personalized measurement and game theory. This model can be separated into two parts: an offline recommendation part (OFFLRP) and an online recommendation part (ONLRP). In the offline part, we emphasize users personalization. We introduce two metrics named user pursue-novelty degree (UPND) and music popularity (MP) to improve the traditional items-based collaborative filtering algorithm. In the online part, we try to solve the interest drift problem, which is a thorny problem in the offline part. We propose a novel online recommendation algorithm based on game theory. Experiments verify that the hybrid music recommendation model has higher new music recommendation accuracy, decent dynamical personalized recommendation ability, and real-time recommendation capability, and can substantially mitigate the problem of interest drift.

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Music recommendation algorithm based on user portrait

Cited by 2 publications

References 13 publications

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

A Hybrid Music Recommendation Model Based on Personalized Measurement and Game Theory

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