A new recommendation algorithm combined with spectral clustering and transfer learning

Li, Xiang; Wang, Zhijian

doi:10.1007/s10586-017-1161-4

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…Third, compared to book and movie content, users typically consume a song for a short period of time, and users can tolerate repeated song recommendations. Fourth, there is relatively little explicit rating data in music platforms [ 9 ]. The construct of the bullet form is to process the data that the recommendation algorithm relies on and build suitable features for the recommendation algorithm to use.…”

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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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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“…Li et al devised a collaborative combined spectral clustering and transfer learning filtering recommendation algorithm. This approach effectively addressed the challenges of data sparsity and the lack of knowledge transfer between multiple matrices [17] .…”

Section: Jiang Et Al Proposed a New Collaborative Filtering Methodsmentioning

confidence: 99%

Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path

Yi,

Tian,

Zhao

2023

IEEE Access

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Cultural communication plays a vital role in social development and human interaction. Red culture, as an integral part of China's revolutionary history and socialist construction, holds significant meaning and exerts a wide influence. However, in the era of information technology, effectively disseminating red culture and stimulating public interest and participation has become an urgent challenge. In this study, we use the advanced deep learning tech to explore the use of multimodal data fusion for enhancing the effectiveness and impact of red culture communication. Specifically, we extract text features and image features from users' browsing information using BI-GRU and CNN, respectively. These features are then fused with user portraits to create a multisource information fusion vector. Subsequently, we employ a BPNN (Backpropagation Neural Network) to perform user interest classification based on the fused features. Experimental results demonstrate that our proposed user recognition framework achieves an average recognition rate of 95.4% across three types of users, indicating high accuracy. Therefore, the user interest classification model, incorporating fused multi-features, presented in this paper offers a promising approach for future red culture communication, as well as user intelligent recommendation and analysis. KEYWORD red culture; Cultural transmission; BI-GRU; CNN; feature fusion; 1.INTRODUCTIONRed culture holds a significant position and exerts a profound influence on modern Chinese culture. It serves as not

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“…In these scenarios, spectral clustering outperforms popular Euclidean clustering techniques, such as K-means clustering [5], [6]. Hence, spectral clustering has found applications in various domains, including computer vision [7]- [9], biology [10], neuroscience [11], recommender systems [12], and blind source separation [13].…”

Section: Introductionmentioning

confidence: 99%

Scalable Spectral Clustering With Nyström Approximation: Practical and Theoretical Aspects

Pourkamali-Anaraki

2020

IEEE Open J. Signal Process.

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Spectral clustering techniques are valuable tools in signal processing and machine learning for partitioning complex data sets. The effectiveness of spectral clustering stems from constructing a non-linear embedding based on creating a similarity graph and computing the spectral decomposition of the Laplacian matrix. However, spectral clustering methods fail to scale to large data sets because of high computational cost and memory usage. A popular approach for addressing these problems utilizes the Nyström method, an efficient sampling-based algorithm for computing low-rank approximations to large positive semi-definite matrices. This paper demonstrates how the previously popular approach of Nyström-based spectral clustering has severe limitations. Existing time-efficient methods ignore critical information by prematurely reducing the rank of the similarity matrix associated with sampled points. Also, current understanding is limited regarding how utilizing the Nyström approximation will affect the quality of spectral embedding approximations. To address the limitations, this work presents a principled spectral clustering algorithm that exploits spectral properties of the similarity matrix associated with sampled points to regulate accuracy-efficiency trade-offs. We provide theoretical results to reduce the current gap and present numerical experiments with real and synthetic data. Empirical results demonstrate the efficacy and efficiency of the proposed method compared to existing spectral clustering techniques based on the Nyström method and other efficient methods. The overarching goal of this work is to provide an improved baseline for future research directions to accelerate spectral clustering.

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A new recommendation algorithm combined with spectral clustering and transfer learning

Cited by 9 publications

References 32 publications

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

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

Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path

Scalable Spectral Clustering With Nyström Approximation: Practical and Theoretical Aspects

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