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recommendation algorithm Integrating CNN and Attention System in Data Extraction

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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recommendation algorithm For Potential Candidates In Human Resources Based On The Integration Of Dynamic Decision Diagram

Recommendation Algorithm For Potential Candidates In Human R...

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International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Gegentana Hulunbuir Univ Hulunbuir 021000 Inner Mongolia Peoples R China

ISBN: (纸本)9781728144634

A recommendation algorithm is put forward for the potential candidate recommendation technology in human resources. The dynamic decision diagram is combined in this method based on collaborative filtering technology and partition clustering technology. Firstly, the computational matrix that integrates the dynamic decision diagram recommendation algorithm is established so that the algorithm can recommend information in reference to the matrix. Secondly, the assignment range of the matrix is improved, so that the recommendation algorithm can integrate the evaluations of all users. Finally, the evaluation value and the update coefficient are added, which makes the dynamic update of the algorithm possible, thus recommending the most satisfactory information to the users. On this basis, an advanced recommendation algorithm based on partition clustering is proposed, which has further improved the precision and real-time performance of the algorithm. In addition, experiments are carried out to verify that the ultimate commendation algorithm based on partition clustering is the optimal recommendation algorithm for potential candidates in human resources, which can provide the most satisfactory information to users.

关键词： Potential Candidates in Human Resources recommendation algorithm Dynamic Decision Diagram

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recommendation algorithm for Mobile E-commerce Based on Reputation 19

Recommendation Algorithm for Mobile E-commerce Based on Repu...

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11th International Conference on Machine Learning and Computing (ICMLC)

作者： Chai, Yuan Li, Dong Wu, Yuchen Qinghai Univ Xining Qinghai Peoples R China Southwest Petr Univ Chengdu Sichuan Peoples R China

ISBN: (纸本)9781450366007

In the new technical background, more and more goods appear in front of the user. Unfortunately, users are increasingly easy to get lost in the massive commodity information. In order to improve the users experience of mobile e-commerce, combined with the advanced technology of large data age, there have been many recommendation algorithms for mobile e-commerce. All of this recommendation algorithms based on customer interests, sales and other different considerations, in order to achieve a purposeful and efficient screening recommendation information. In many ways of thinking, the user reputation as a new era of the spirit of the contract reflects the important value. In this paper, we focus on the current technical situation, designing a set of scoring algorithm through the analysis of the previous behavioral data modeling. After this algorithm, from different customer categories of different needs to start using the mainstream for the score of the business recommendation algorithm algorithm to achieve. And the results are compared with the expected analysis in order to obtain the current algorithm in the credibility of the algorithm under the improvement and adjustment. Finally, we will design a set of recommendation algorithm based on the reputation standards.

关键词： recommendation algorithm collaborative filtering reputation

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recommendation algorithm Based on Multi-Label Clustering and Core Users 22

Recommendation Algorithm Based on Multi-Label Clustering and...

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22nd IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Liu, Peiyu Wang, Peipei Wang, Ru Liu, Wanfeng Shandong Normal Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781538614822

With the rapid growth of user scale, it is so meaningful to explore the users who carry more valuable information in the user group. The importance of the individual users in the recommender system can improve the recommendation efficiency of the recommender system, enhance the robustness of the recommender system,but there is little research work in this path and can not determine a more effective *** solve this problem, we propose a method based on multi-label clustering to determine core user,and define the concept of correlation between user and label cluster , user location weight,considered the potential relationship between users and *** the same time, we proposed a new method based on multi-label clustering and core user,according to experiments demonstrate the effectiveness of the proposed algorithm, we have a more significant upgrade in the recommendation accuracy and diversity.

关键词： Multi-label Clustering Density Peak Clustering Core User recommendation algorithm

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recommendation algorithm Based on Refined Knowledge Graphs and Contrastive Learning 17th

Recommendation Algorithm Based on Refined Knowledge Graphs a...

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17th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Wang, Bing Yang, Xiaoling Zhang, Xingpeng Zhao, Chunlan Wang, Chunhao Feng, Weishan Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu Peoples R China Southwest Petr Univ Sch Sci Chengdu Peoples R China

ISBN: (纸本)9789819755004;9789819755011

Knowledge graphs are a vital tool for improving recommendation performance and interpretability since they are rich in auxiliary data. Two obstacles must be overcome in the recommendation process, though: 1) Real-world knowledge graphs are frequently noisy and contain connections unrelated to items and entities;2) Data sparsity is a problem due to the long-tail distribution of user-item interactions. To address the aforementioned issues, a recommendation algorithm based on refined knowledge graphs and contrastive learning (RKGCL) is proposed. Firstly, the algorithm utilizes a graph pruning strategy to trim task-irrelevant knowledge associations, obtaining a high-quality knowledge graph. Secondly, it employs a graph convolutional network based on attention mechanisms to learn item embeddings. Subsequently, a simple yet effective noise-based embedding enhancement is applied for crosslayer contrastive learning, thereby alleviating the data sparsity issue. Finally, a message propagation strategy is employed to obtain user and item embeddings for recommendation prediction. Experimental results on three public datasets - Yelp, amazon-book, and Last-FM - show that our model outperforms other benchmark models on Recall@K and NDCG@K.

关键词： recommendation algorithm Knowledge Graph Contrastive Learning Graph Convolutional Networks

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recommendation algorithm Based on Federated Multi-modal Learning 49

Recommendation Algorithm Based on Federated Multi-modal Lear...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Feng, Chenyuan Feng, Zhenyu Wang, Qing Shenzhen Univ Guangdong Hong Kong Joint Lab Big Data Imaging & Shenzhen Peoples R China ZTE Corp Natl Key Lab Mobile Network & Mobile Multimedia T Shenzhen Peoples R China

ISBN: (纸本)9798350374520;9798350374513

recommendation algorithm is a crucial tool for personalized information filtering. However, traditional recommendation algorithms typically need the collection of raw user data in a centralized server for analysis. Deploying the recommendation algorithm in a federated learning (FL) manner is a promising solution, which can train a good-quality model on the premise that the server does not directly access the sensitive user data, in light of the growing attention to privacy security. Nevertheless, the majority of recommendation algorithm researches has only considered uni-modality learning. To fill the gap, this paper designs a federated multi-modal RS. Firstly, an adaptive multi-modal federated recommendation algorithm is proposed to extract features from the item image, the commodity introductory text, and user historical ratings. The algorithm also address the issue of missing modalities via a modal completion mechanism. Additionally, an attention mechanism is designed to distinguish the importance of various modal data on model training, wherein different weight ratios are assigned to various features to realize an adaptive multi-modal RS. Furthermore, one multi-modal dataset is established to verify the efficacy of the proposed algorithm. The simulation results show that our proposed measures contribute a reliable and high-quality recommendation model.

关键词： Federated Learning multi-modal learning recommendation algorithm attention mechanism

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recommendation algorithm for agricultural and sideline products on e-commerce platforms based on deep learning

Recommendation algorithm for agricultural and sideline produ...

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Chen, Lisi School of Computer Science and Engineering Guangzhou Institute of Science and Technology China

ISBN: (纸本)9798331520816

Aiming at the problem of low accuracy in e-commerce platform recommendation of agricultural and sideline products, an agricultural and sideline product recommendation algorithm based on deep learning was studied. Fully analyze the problems existing in the process of recommending agricultural and sideline products, study the mechanism of item-based collaborative filtering algorithm, and combine the document topic and matrix decomposition hybrid algorithm. Highlighting the advantages of both, a topic weighted fusion collaborative filtering algorithm was formed. Through experiments, this algorithm can effectively improve the recommendation effect of agricultural and sideline products on e-commerce platforms. Compared with the simple item collaborative filtering algorithm, the algorithm studied in this study can effectively improve the accuracy of recommended targets by about 5%, and also has a 2% improvement in the diversity of recommended targets. In terms of accuracy of prediction results, through experimental comparison, the root mean square error of this algorithm is also lower than other comparative algorithms, which proves the advantages of this algorithm. © 2024 IEEE.

关键词： Agricultural and sideline products E-commerce platform New media recommendation algorithm

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recommendation algorithm Based On Tag Time Weighting 3

Recommendation Algorithm Based On Tag Time Weighting

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3rd International Conference on Smart City and Systems Engineering (ICSCSE)

作者： Liu Feng Guo Wei-wei Heilongjiang Univ Technol Jixi 158100 Peoples R China

ISBN: (纸本)9781728113661

There are two problems in recommendation System: data sparseness and cold start. To deal with the problems of cold start and recommendation accuracy caused by the "information overload" of the computer network, the author came up with a recommendation algorithm which is based on tag time weighting The algorithm can effectively alleviate the cold start problem. First, the content and implementation process of the algorithm were introduced. Then, using the data sets, the proposed time-weighted label network structure algorithm, also called as TT-WN, was compared with the traditional collaborative filtering algorithm) and the classical network structure algorithm. Finally, the experimental results were compared and analyzed. The results showed that the proposed new time-weighted label network structure algorithm (TT-WN) did a good job in the experimental data sets, and the hit rate and sorting accuracy were much better than the classical network structure algorithm and the traditional collaborative filtering algorithm algorithms. Naturally, the conclusion that the TT-WN algorithm has many advantages was achieved.

关键词： recommendation System Time Weight Label System recommendation algorithm

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