A Group-Oriented Recommendation Algorithm Based on Similarities of Personal Learning Generative Networks

Zhu, Haiping; Ni, Yifu; Tian, Feng; Feng, Pingfa; Chen, Yan

doi:10.1109/access.2018.2856753

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…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. This is due to some unique peculiarities in digital music marketing: first, song audio is the main influencing factor when users consume music [ 7 ]. Psychological research results show that in most cases, whether a user likes a song depends largely on the characteristics of the song's vocal, melody, rhythm, timbre, genre, or instrumentation, etc.…”

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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“…In that case, an advertisement should be recommended to target users who have the possibility to purchase. Similar ideas have appeared in work on group recommender systems where the goal is to find recommendations that can maximize the utility of users in each group [18].…”

Section: A Multistakeholder Recommender Systemsmentioning

confidence: 86%

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Wang

Chen

2020

IEEE Access

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Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers' preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users' views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods. INDEX TERMS Many-objective, recommender systems, similarity model, stakeholders.

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“…Currently, DELPHOS does not explain how the best aggregation strategy is utilized to generate the final rating. [49] Group…”

Section: Methodologies/ Techniquesmentioning

confidence: 99%

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

G. M.,

Goudar,

Kulkarni

et al. 2024

IEEE Access

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This review delves into using e-learning technology and personalized recommendation systems in education. It examines 60 articles from prominent databases and identifies the different methods used in recommendation systems, such as collaborative and content-based approaches with a recent shift towards machine learning. However, the current personalized recommendation system faces challenges such as a lack of understanding of the content, student discontinuity, language barriers, confusion in selecting study materials, inadequate infrastructure and funding. The review proposes using new digital technologies to address these issues, including Fluxy AI, Twin technology, AI-powered virtual proctoring, and Alter Ego. These technologies can create a dynamic and interactive learning environment, providing tailored learning experiences for students and insights for educators to provide targeted support and guidance. The integration of these technologies can improve individualized learning, increase understanding capacity and enhance the learning experience for students with speech disorders.

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A Group-Oriented Recommendation Algorithm Based on Similarities of Personal Learning Generative Networks

Cited by 16 publications

References 22 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

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

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