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4th International Conference on Data and Information in Online Environments

作者： Mao, Chengzhi Wu, Zhifeng Tianjin Univ Technol & Educ Sch Informat Technol Engn Tianjin Peoples R China

ISBN: (纸本)9783031807121;9783031807138

Recommender system is widely used in e-commerce, news consulting, social networking, tourism, film, music and other fields because it can effectively deal with the problem of information overload. According to the practical problems in the recommendation system, various recommendation algorithms are produced. Traditional recommendation algorithms are divided into content-based recommendation, collaborative filtering based recommendation and hybrid recommendation. Collaborative filtering algorithms are favored because they can extract and process relevant information features to accurately predict user preferences. However, collaborative filtering algorithms generally have problems such as sparse data, cold start and data scalability. This paper presents the Associated Residual Matrix Factorization (ARMF) model, which can solve the common cold start problem and improve the recommended performance. The model uses associative residual neural networks (ARNs) to model higher-order interactions between the user and the underlying features of the item and to avoid network layer gradients exploding or disappearing and overfitting problems. In order to verify the validity and correctness of the model, this paper conducts tests on Movielens100k and Movielens10M datasets, and the experimental results show that the prediction results are 17%similar to 23% better than other comparison algorithms.

关键词： recommendation algorithm Matrix factorization residual network

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Personalized recommendation algorithm Based on Knowledge Graphs with High-Order Information 42nd

Personalized Recommendation Algorithm Based on Knowledge Gra...

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42nd National Conference on Theoretical Computer Science

作者： Zhang, Siyao Wang, Zhihui Hu, Jinru Chen, Jianrui Shaanxi Normal Univ Sch Comp Sci Xian 710199 Peoples R China

ISBN: (纸本)9789819614899;9789819614905

With the rapid development of network technology, recommendation systems attract increasing research because of its wide applications in e-commerce. Nevertheless, most existing recommendation models based on graph neural networks do not consider the transitivity of subgraph structures in interactive data. This makes the models unable to capture the complex dependencies and mutual influences between users and items, resulting in the inability to achieve high-quality personalized recommendations. To address the above challenge, we propose a novel recommendation algorithm based on knowledge graph with high-order graph convolutional network, named KG(2)CN. Firstly, we introduce the subgraph structure on the knowledge graph to capture high-order contextual information between users and items. Secondly, the mined subgraph information and graph convolutional network are combined to learn high-order features of users and items. Finally, the decoder is applied to predict the ratings of target users on the uninteracted items, thereby recommending the Top-K items. Experimental results on the Book-Crossing and *** datasets show that the proposed KG(2)CN obtains better performance in F1 score and AUC metrics.

关键词： recommendation algorithm Knowledge Graph Graph Convolutional Network High-order Pattern

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Analysis and Application of Spark Fast Data recommendation algorithm Based on Hadoop Platform

Analysis and Application of Spark Fast Data Recommendation A...

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2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Yan, Jialu Decoded Advertising New York10005 United States

ISBN: (纸本)9798331504205

With the rapid advancement of information technology, efficient management and precise formulation of personalized recommendations have become the focus of attention in many fields. This article explains the core principles of Hadoop architecture and Spark technology, as well as the mechanism of their joint operation. We analyzed the multi-level architecture of Spark technology and its parallel processing model's contribution to driving algorithm performance. In terms of algorithm construction, the implementation process of a recommendation algorithm based on collaborative filtering and content matching on the Spark platform was described in detail, and its excellent performance was confirmed through experimental methods. We discussed the application examples of Hadoop Spark platform in multiple fields such as e-commerce, social networks, financial services, and online teaching, demonstrating the effectiveness of the algorithm in improving recommendation accuracy and accelerating system feedback speed. © 2025 IEEE.

关键词： Fast data Hadoop platform recommendation algorithm Spark

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Exploring Factors Driving Satisfaction from recommendation algorithm on Netflix

Exploring Factors Driving Satisfaction from Recommendation A...

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International Conference on Marketing and Technologies, ICMarkTech 2023

作者： Olma, Karolina Rizun, Mariia Strzelecki, Artur Department of Informatics University of Economics in Katowice 1 Maja 50 40-287 Katowice Poland

ISBN: (纸本)9789819736973

The present study aims to evaluate customer satisfaction with the recommendation algorithm used by Netflix. Online streaming platforms have revolutionized the way people consume media content. With the multiplication of choices and the abundance of content, it has become difficult for users to find the materials they are interested in watching. To address this problem, recommendation algorithms have been developed to suggest the content that the user is most likely to find entertaining. The research is conducted through structural modeling equations and data analysis, with the use of data from 274 Netflix users. The results show a positive relationship between personalization, use of search engine, homepage usefulness and satisfaction from using Netflix’s recommendation algorithm. The results of this research contribute to our understanding of customer satisfaction with the recommendation system of online streaming platforms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Media content Netflix recommendation algorithm recommendation system

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Deep Learning Aided SID in Near-Field Power Internet of Things Networks With Hybrid recommendation algorithm

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COMPUTATIONAL INTELLIGENCE 2025年第1期41卷

作者： Chen, Chuangang Wu, Qiang Wang, Hangao Chen, Jing Elect Power Res Inst Hainan Power Grid Co Ltd Haikou Peoples R China Joint Lab Smart Grid & Isl Microgrid Haikou Peoples R China

In the realm of power Internet of Things (IoT) networks, secure inspection detection (SID) is paramount for maintaining system integrity and security. This paper presents a novel framework that leverages deep learning-based semi-autoencoders in conjunction with a hybrid recommendation algorithm to enhance SID tasks. Our proposed method utilizes the deep learning-based semi-autoencoder to effectively capture and learn complex patterns from high-dimensional power IoT data, facilitating the identification of anomalies indicative of potential security threats. The hybrid recommendation algorithm, which combines collaborative filtering and content-based filtering, further refines the detection process by cross-verifying the identified anomalies with historical data and contextual information, thereby improving the accuracy and reliability of the SID tasks. Through extensive simulations and practical data evaluations, our proposed framework demonstrates superior performance over conventional methods, achieving higher detection accuracy. In particular, the detection accuracy of the proposed scheme is more than 20% higher than that of the competing schemes.

关键词： deep learning IoT networks recommendation algorithm semi-autoencoder

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Short video content and recommendation algorithm based on deep learning

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Yang, Yang Manzhouli Russian Vocat Coll Chinese Dept 6Huafeng St Manzhouli City Inner Mongolia Peoples R China

Existing short video content analysis methods often struggle with integrating multimodal data, resulting in incomplete content understanding and limited recommendation accuracy. To address these challenges, this study proposes a deep learning-based short video content analysis and recommendation algorithm that enhances feature extraction and personalization. CNN and LSTM are employed to automatically capture and extract multimodal features from short videos, generating a comprehensive feature representation through weighted fusion. Meanwhile, DNN is utilized to model user behavior, extracting deep behavioral features to enhance preference prediction. By jointly modeling video content and user behavior, a multimodal recommendation algorithm is developed to optimize content delivery. Experimental results show that, compared to CF, MF, and Content-Based Filtering benchmark algorithms, the proposed method improves average precision by 4.7%, 3.3%, and 4.0%, and recall by 3.5%, 1.2%, and 2.1%, respectively. These findings confirm that the deep learning-driven approach effectively enhances multimodal content understanding, meets personalized user preferences, and significantly improves recommendation accuracy, offering a more intelligent and adaptive short video recommendation framework.

关键词： Short video content analysis recommendation algorithm deep learning Deep Neural Network

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recommendation algorithm of Industry Stock Trading Model with TODIM

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第3期23卷 1301-1334页

作者： Lv, Dongdong Gong, Yingli Chen, Jianting Xiang, Yang Ningbo Univ Technol Sch Cyber Sci & Engn Ningbo 315211 Peoples R China Tongji Univ Sch Econ & Management Shanghai 201804 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China's A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long-short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets.

关键词： Stock trading recommendation algorithm TODIM trading strategy

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recommendation algorithm Integrating CNN and Attention System in Data Extraction

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Computers, Materials & Continua 2023年第5期75卷 4047-4063页

作者： Yang Li Fei Yin Xianghui Hui College of Information and Management Science Henan Agricultural UniversityZhengzhou450046China

With the rapid development of the Internet globally since the 21st century,the amount of data information has increased *** helps improve people’s livelihood and working conditions,as well as learning ***,data extraction,analysis,and processing have become a hot issue for people from all walks of *** recommendation algorithm still has some problems,such as inaccuracy,less diversity,and low *** solve these problems and improve the accuracy and variety of the recommendation algorithms,the research combines the convolutional neural networks(CNN)and the attention model to design a recommendation algorithm based on the neural network *** the text convolutional network,the input layer in CNN has transformed into two channels:static ones and non-static ***,the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes *** recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction *** obtains data name features through a convolution ***,the top pooling layer obtains the length *** attention system layer obtains the characteristics of the data *** results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present *** proposed algorithm shows excellent accuracy and robustness.

关键词： Data extraction recommendation algorithm CNN algorithm attention model

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recommendation algorithm based on item quality and user rating preferences

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Frontiers of Computer Science 2014年第2期8卷 289-297页

作者： Yuan GUAN Shimin CAI Mingsheng SHANG Web Sciences Center School of Computer Science and EngineeringUniversity of Electronic Science and Technology of China Chengdu 611731 China

Recommender systems are one of the most im- portant technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filter- ing CF family, have problems ness. These problems hinder such as scalability and sparse- further developments of rec- ommender systems. We propose a new recommendation al- gorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accu- racy in rating prediction compared with the traditional ap- proaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.

关键词： recommendation algorithm item quality userrating preferences RMSE

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recommendation algorithm based on improved spectral clustering and transfer learning

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PATTERN ANALYSIS AND APPLICATIONS 2019年第2期22卷 633-647页

作者： Li, Xiang Wang, Zhijian Hu, Ronglin Zhu, Quanyin Wang, Liuyang Huaiyin Inst Technol Fac Comp & Software Huaian 223003 Peoples R China Hohai Univ Coll Comp & Informat Technol Engn Nanjing 211100 Jiangsu Peoples R China

Collaborative filtering (CF) recommendation has made great success in solving information overload. However, CF has some disadvantages such as cold start, data sparseness, low operation efficiency and knowledge cannot transfer between multiple rating matrixes. In this paper, we propose a recommendation algorithm based on improved spectral clustering and transfer learning (RAISCTL) to improve the forecasting accuracy and generalization ability of recommender system. RAISCTL firstly improves the spectral clustering by using the eigenvalue differences and orthogonal eigenvectors and realizes the automatic determination of cluster numbers. In addition, the improved spectral clustering algorithm is used to cluster the two dimensions of the users and items of the original rating matrix. Then, RAISCTL decomposes the rating matrix after clustering and gets the sharing group rating matrix. Finally, RAISCTL makes rating forecasting and recommendations based on the sharing group rating matrix and transfer learning. The simulation results show that RAISCTL can effectively improve the recommendation accuracy and generalization ability compared with other 8 conventional CF approaches.

关键词： Spectral clustering recommendation algorithm Recommender systems Collaborative filtering Transfer learning

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