A collaborative filtering recommendation algorithm based on the influence sets of e-learning group’s behavior

Xiu-ju, Liu

doi:10.1007/s10586-017-1560-6

Cited by 35 publications

(20 citation statements)

References 12 publications

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“…Foreign media have been incorporating sensor data into news reports since May 2013, when the or Digital News Center of Columbia University proposed the concept of "SN." According to the literature [12], sensors will have a lot of potential in the field of news communication in the future.…”

Section: Related Workmentioning

confidence: 99%

Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

Jin

Sun

2022

Wireless Communications and Mobile Computing

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With the vigorous development of new media technologies such as Internet of Things, big data, and cloud computing, data-based sensor news (SN) will become the trend of news reporting in the future and the new normal of news production. Under this background, this paper further analyzes the relationship between SN production mode and traditional news production, including the inheritance of traditional news production value concept, as well as the breakthrough and change in form, media, and effect. In this paper, collaborative filtering (CF) algorithm is improved to solve the problems of data sparseness, user interest migration, and scalability in CF technology. In the calculation of news content similar degree (SD), the influence of part of speech and position of feature words in news is also considered, and the time window is used to establish a model that adapts to the change of user interest with time. In this method, the contribution degree of different attributes to distinguishing users is considered, and the attribute SD between users is accurately calculated, which effectively improves the accuracy of SN data acquisition results.

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Section: Related Workmentioning

confidence: 99%

Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

Jin

Sun

2022

Wireless Communications and Mobile Computing

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“…In order to improve the quality of listening services and user satisfaction, we need to find a marketing method that can meet users' personalized listening needs and help them make decision support in the vast amount of information, i.e., presenting only a small amount of music content that matches users' preferences in a limited field of view, so as to finally achieve the marketing strategy goal of increasing the size of users and the penetration rate of payment [ 5 ]. Existing research on recommendation systems has focused on movies, books, news, and e-commerce, enabling online platforms to recommend high-quality content or items to users more than ever before, achieving the marketing goal of reducing costs and increasing efficiency [ 6 ]. However, these research findings have not worked well in digital music marketing in terms of recommendations; i.e., lower recommendation accuracy and content coverage rates have emerged.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Learning Recommendation Algorithm Based on Improved BP Neural Network in Music Marketing Strategy

2021

Computational Intelligence and Neuroscience

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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.

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“…However, auxiliary information often has problems such as large scale, multiple types, inconsistent data types, and missing key data. e hybrid recommendation is facing severe challenges [14,15].…”

Section: Related Workmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm for MOOC Resources Based on Deep Learning

2021

Complexity

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In view of the poor recommendation performance of traditional resource collaborative filtering recommendation algorithms, this article proposes a collaborative filtering recommendation model based on deep learning for art and MOOC resources. This model first uses embedding vectors based on the context of metapaths for learning. Embedding vectors based on the context of metapaths aggregate different metapath information and different MOOCs may have different preferences for different metapaths. Secondly, to capture this preference drift, the model introduces an attention mechanism, which can improve the interpretability of the recommendation results. Then, by introducing the Laplacian matrix into the prior distribution of the hidden factor feature matrix, the relational network information is effectively integrated into the model. Finally, compared with the traditional model using the scoring matrix, the model in this article using text word vectors effectively alleviates the impact of data sparsity and greatly improves the accuracy of prediction. After analyzing the experimental results, compared with other algorithms, the resource collaborative filtering recommendation model proposed in this article has achieved better recommendation results, with good stability and scalability.

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A collaborative filtering recommendation algorithm based on the influence sets of e-learning group’s behavior

Cited by 35 publications

References 12 publications

Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

[Retracted] Learning Recommendation Algorithm Based on Improved BP Neural Network in Music Marketing Strategy

Collaborative Filtering Recommendation Algorithm for MOOC Resources Based on Deep Learning

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