Research on music culture personalized recommendation based on factor decomposition machine

Xu, Dazhi

doi:10.1007/s00779-019-01343-9

Cited by 31 publications

(15 citation statements)

References 22 publications

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“…n . Because the score of students on courses in the data set is 5 integers within the range [1,5], the range of S n+1 at all times constitutes the action space C:…”

Section: Markov Decision-making Process Markov Decisionmentioning

confidence: 99%

“…Recommendation system can actively provide personalized information for users. At present, personalized recommendation has been widely used in social networking [3], news, music, books, and movies [4], such as cloud music [5] and online shopping product recommendation [6].…”

Section: Introductionmentioning

confidence: 99%

“…(p)n is[1,5]. e state S (p) n under all timestamps constitutes the state space S.(2) For action space C, student U looks at the course under time n and gives a grade S…”

mentioning

confidence: 99%

“…Once the action C (p) n is determined, the next state S (p)n is also determined. At this point, the transition probability between states is determined; that is,C (p) n � S (p) n+1 , U � 1. e value range of the action C (p)n is[1,5]. (4) For discount factor ?, in the model, each action generates a corresponding reward.…”

mentioning

confidence: 99%

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Ideological and Political Education Recommendation System Based on AHP and Improved Collaborative Filtering Algorithm

Wang

2021

Scientific Programming

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Aiming to solve the problem that ideological and political education courses in universities are not targeted enough and cannot form personalized recommendations, this paper proposes an ideological and political education recommendation system based on analytic hierarchy process (AHP) and improved collaborative filtering algorithm. Firstly, considering the time effect of student scoring, the recommendation model is transformed into Markov decision process. Then, by combining the collaborative filtering algorithm with reinforcing learning rewards and punishments, an optimization model of student scoring based on timestamp information is constructed. To quantify the degree of students' preference for courses, the analytic hierarchy process is used to convert the students' behavior data into the preference value of courses. To solve the problem of data scarcity, the missing values are predicted by the prediction score rounding filling and the optimization boundary completion method. Experimental results show that the feasibility of the proposed system is verified, and the system has vital accuracy and convergence performance. The ideological and political education recommendation system proposed in this paper has important reference significance for promoting ideological and political education in the era of big data.

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“…n . Because the score of students on courses in the data set is 5 integers within the range [1,5], the range of S n+1 at all times constitutes the action space C:…”

Section: Markov Decision-making Process Markov Decisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…(p)n is[1,5]. e state S (p) n under all timestamps constitutes the state space S.(2) For action space C, student U looks at the course under time n and gives a grade S…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Ideological and Political Education Recommendation System Based on AHP and Improved Collaborative Filtering Algorithm

Wang

2021

Scientific Programming

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“…Since the idea of this technology was first proposed in 1992, collaborative filtering technology has attracted more and more scholars' attention due to its broad application value. Meanwhile, with the launch of Netflix Prize [10,11], the collaborative filtering algorithm has been promoted to one of the most active research fields in machine learning. Among the many recommendation technologies, content-based recommendation and collaborative filtering recommendation are the most studied.…”

Section: Introductionmentioning

confidence: 99%

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Hui¹,

2020

Complexity

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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.

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User interest modeling and collaborative filtering algorithms application in English personalized learning resource recommendation

Jin

2023

Soft Comput

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In order to solve the problem that the current traditional English classroom teaching in large classes has higher teaching e ciency, but it is di cult to take care of every student, and it is di cult to meet the needs of students' personalized learning, this paper combines user interest modeling and collaborative ltering algorithms to propose a knowledge-oriented learning resource recommendation method.Moreover, this paper proposes an algorithm based on user interest model. In addition, on the basis of obtaining the user's historical interest model, this paper combines user behavior information to obtain the user interest model and calculates the similarity between the candidate items and the user, and makes TOP-N recommendation based on the similarity calculation result. Finally, this paper conducts experiments on the news dataset, and compares the results with the benchmark algorithm to prove the effectiveness of the algorithm. The results show that this paper has achieved certain results in the research of combining multi-data source user interest modeling and recommendation and has made a little contribution to the research of interest modeling.

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Research on music culture personalized recommendation based on factor decomposition machine

Cited by 31 publications

References 22 publications

Ideological and Political Education Recommendation System Based on AHP and Improved Collaborative Filtering Algorithm

Ideological and Political Education Recommendation System Based on AHP and Improved Collaborative Filtering Algorithm

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

User interest modeling and collaborative filtering algorithms application in English personalized learning resource recommendation

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