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recommendation algorithm of Industry Stock Trading Model with TODIM

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 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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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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A Novel recommendation algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network

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Big Data Mining and Analytics 2024年第2期7卷 357-370页

作者： Qiang Sun Leilei Shi Lu Liu Zixuan Han Liang Jiang Yan Wu Yeling Zhao School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK School of Ocean College Jiangsu University of Science and TechnologyZhenjiang 212003China School of Finance and Economics Jiangsu UniversityZhenjiang 212013China

Grid-based recommendation algorithms view users and items as abstract nodes,and the information utilised by the algorithm is hidden in the selection relationships between users and *** these relationships can be easily handled,much useful information is overlooked,resulting in a less accurate recommendation *** aim of this paper is to propose improvements on the standard substance diffusion algorithm,taking into account the influence of the user’s rating on the recommended item,adding a moderating factor,and optimising the initial resource allocation vector and resource transfer matrix in the recommendation *** average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation *** are conducted on the MovieLens training dataset,and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.

关键词： cloud computing link prediction bipartite graph network recommendation algorithm cold start problem

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Multimodal recommendation algorithm based on Dempster-Shafer evidence theory

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第10期83卷 28689-28704页

作者： Wang, Xiaole Qin, Jiwei Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Signal Detect & Proc Urumqi 830046 Peoples R China

Multimodal features of items (e.g., text, audio, visual) have been proven to be effective in improving recommendations. Most of the existing personalized recommendation algorithms only use user-item interaction to recommend items for users, ignoring the rich auxiliary information implicit in the multimodal features of items. This auxiliary information can enrich the user's description of the item and enhance the mining ability of the recommendation algorithm. However, as a result of users having different preferences for each modality, the multimodal recommendation becomes a challenging task. Therefore, this paper proposes a multimodal recommendation algorithm based on the Dempster-Shafer evidence theory. First, we use the similarity measure of user history interaction items and non-interaction items in different modalities to characterize user preferences for noninteraction items. Second, we use the discrete degree of different modal features of user history interaction items to represent the degree of user preference for different modalities. Third, according to the Dempster-Shafer evidence theory, users' preferences for noninteractive items in different modalities are considered evidence. As a result, we fuse this evidence with different modal preferences to produce recommendations. Experimental results show that the proposed method is superior to the traditional personalized recommendation algorithm and the early multimodal fusion recommendation method. The proposed algorithm has good interpretability and expansibility.

关键词： recommendation algorithm Multimodal Evidence theory Fusion

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A crowd-sourcing recommendation algorithm OPCA-CF using outer-product co-attention mechanism

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年第1期39卷 102-124页

作者： Bi, Kejun Liu, Jingwen Zhao, Qiwen Chen, Yanru Xing, Bin Guo, Bing Sichuan Univ Sch Comp Sci Chengdu Peoples R China Chongqing Innovat Ctr Ind Big Data Co Ltd Chongqing Peoples R China

With the rapid development of information technology, crowd-sourcing technology is increasingly used in non-invasive monitoring in smart cities. Applying recommendation algorithms in crowd-sourcing can optimise resource allocation, improve task-matching accuracy and enhance participant satisfaction, whereas existing recommendation algorithms cannot be directly applied in crowd-sourcing, as such scenarios have unique features, such as task timeliness and multi-role users. Designed explicitly for crowd-sourcing scenarios, our OPCA-CF (Outer-product Co-attention Collaborative Filtering) algorithm is formed by an upgraded ItemCF (Item-based Collaborative Filtering) algorithm as main-network and OPCA (Outer-product Co-attention) mechanism as a sub-network. Firstly, ItemCF is improved through attribute-level task feature learning, new-role feature and weighted cross-entropy in the loss function. Most importantly, we propose OPCA using outer-product, while the existing co-attention mechanism only uses inner-product. Compared with the best existing algorithm using real-world datasets, OPCA-CF's performance is proved to be superior by 1.24%, 4.25% and 5.35%, with binary classification indicators AUC (Area under Curve), recommended Lists related indicators HR (Hit Ratio) and MRR (Mean Reciprocal Rank), respectively. All the performance indicators verified the effectiveness of the OPCA-CF algorithm.

关键词： recommendation algorithm attention mechanism crowd-sourcing collaborative filtering

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A Privacy-Preserving Cross-Domain recommendation algorithm for Industrial IoT Devices

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 227-237页

作者： Yu, Xu Peng, Qinglong Lv, Hongwu Zhan, Dingjia Hu, Qiang Du, Junwei Gong, Dunwei China Univ Petr Qingdao Inst Software Qingdao 266580 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

recommendation algorithms have been initially applied on the online business platform of industrial Internet of Things (IoT) devices. However, traditional recommendation algorithms are often difficult to solve the data sparsity problem. In fact, online shoppers are often accompanied by consumption behavior of other heterogeneous products, so we combine the consumer behavior of other heterogeneous products in the auxiliary domain to improve the recommendation performance of industrial IoT devices in the target domain. Due to privacy-preserving requirements, the original scoring information of the auxiliary domain is often not allowed to be directly shared with the target domain. Therefore, we propose a Privacy-Preserving Cross-Domain recommendation algorithm for industrial IoT devices. First, the non-privacy preference features are extracted through the auxiliary domain scoring data. Next, the extracted preference features are fused with the target domain information. Extensive experiments have been conducted on the Amazon dataset to verify the effectiveness of our method.

关键词： Decoding Privacy Recommender systems Optimization Industrial Internet of Things Data mining Feature extraction Industry 40 industrial Internet of Things privacy-preserving recommendation algorithm

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