Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

Zhang, Yanju; Wang, Yue; Wang, Shiqin

doi:10.1109/access.2020.2980624

Cited by 16 publications

(7 citation statements)

References 28 publications

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“…Considering that the traditional clustering algorithm has the problems of distance calculation and initialization center, this paper improves it in the research, introduces K-means clustering algorithm and Mahalanobis distance, and generates user cluster classification based on scoring data. The attention model is established according to the user access, which makes up for the deficiency of the clustering model [20,21]. Based on user clustering analysis, collaborative filtering recommendation algorithm is used to further mine users' interests and narrow the search space.…”

Section: Optimization Design Of Collaborative Filteringmentioning

confidence: 99%

Core Technology Optimization of Intelligent Financial Technology Based on Collaborative Filtering Algorithm in Big Data Environment

Hou¹

2022

Wireless Communications and Mobile Computing

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With the improvement of people’s economic level, people pay more attention to financial investment. At present, the financial industry provides customers with a variety of investment services, but it has always been unable to provide targeted services for customers. Based on this, this paper studies the optimization of intelligent financial technology core technology based on collaborative filtering algorithm in the big data environment. On the basis of a simple analysis of the application of financial core technology and the research status of collaborative filtering algorithm, this paper constructs an application model of intelligent financial collaborative filtering algorithm. In view of the shortcomings of collaborative filtering algorithm, it uses user-based clustering algorithm to improve the collaborative filtering algorithm. According to the frequency of customers’ access to financial products, the attention model is established and simulated. The results show that the collaborative filtering optimization algorithm used in this paper can reduce the absolute error of recommendation and improve the accuracy.

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Section: Optimization Design Of Collaborative Filteringmentioning

confidence: 99%

Core Technology Optimization of Intelligent Financial Technology Based on Collaborative Filtering Algorithm in Big Data Environment

Hou¹

2022

Wireless Communications and Mobile Computing

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“…Valdiviezo-Diaz et al [6] have built a recommendation engine based on naive bayes classifier. The computation is made by predicting the probability that a particular item will be given a particular rating A new similarity measurement technique is proposed in [7]. Commonly used Pearson correlation and cosine similarity is replaced with intuitionistic fuzzy reasoning.…”

Section: Related Workmentioning

confidence: 99%

Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Palaniappan¹,

Selvaraj²

2022

Computer Systems Science and Engineering

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“…Data Preparation. In order to verify the recognition efficiency of the CFomb method on the users' abnormal behaviors of access to the resources on the operation and maintenance platform of power monitoring system, the behavior of resource access by operation and maintenance users is simulated under the background of operation and maintenance [14][15], and the dataset of resource access by 4 and 5, respectively. It can be seen that when the value of k ranges from 3 to 20, the increase in training time and the decline in RMSE are linear and variable.…”

Section: Experimental Evaluationmentioning

confidence: 99%

A Collaborative Filtering Method for Operation Maintenance Behavior in Power Monitoring Systems

Tao

Zhang

et al. 2022

Journal of Sensors

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As an important part of power infrastructure, a power monitoring system provides real-time data acquisition, state detection, and remote control of power equipment for the power grid and can deal with sudden anomalies in time. The operation and maintenance of the power monitoring system are very important to ensure the stable operation of power grid. The current mainstream remote operation and maintenance mode has internal threats such as misoperation of operation and maintenance personnel or malicious damage caused by attackers stealing operation and maintenance authority. Meanwhile, the existing operation and maintenance audit has the problems of high human resource cost and limited supervision of operation and maintenance personnel. To solve this problem, this paper proposes a collaborative filtering method for operation and maintenance behavior of power monitoring system called CFomb. Exploiting a keyword matching algorithm, CFomb determines the power resources accessed by operation and maintenance users from multiple operation instructions and extracts operation and maintenance behaviors. Referring to the collaborative filtering idea, the feature matrix decomposition scheme is introduced to train the access probability model based on the historical normal behavior of multiple operation and maintenance users, which provides a basis for real-time prediction of the access behavior probability of target operation and maintenance users. The OTSU binarization technique is used to determine the probability threshold of abnormal operation and maintenance behaviors, identify abnormal behaviors through threshold comparison, and send real-time alarms to operation and maintenance audit. The simulation experiment results show that the method in this paper can effectively identify the abnormal behavior of operation and maintenance users, reduce the overhead of manual audit, and help improve the power monitoring system’s ability to respond to internal threats of operation and maintenance.

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Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

Cited by 16 publications

References 28 publications

Core Technology Optimization of Intelligent Financial Technology Based on Collaborative Filtering Algorithm in Big Data Environment

Core Technology Optimization of Intelligent Financial Technology Based on Collaborative Filtering Algorithm in Big Data Environment

Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

A Collaborative Filtering Method for Operation Maintenance Behavior in Power Monitoring Systems

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