Recommender Systems and Collaborative Filtering

Ortega, Fernando; González-Prieto, Ángel

doi:10.3390/app10207050

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…In [17], J. Yu et al investigated collaborative filtering in recommendation systems and proposed an improved method to account for changes in user preferences. X. Jia and F. Liu analyzed the problems of big data processing in commercial systems and improved the real-time recommendation model [18].…”

Section: Related Workmentioning

confidence: 99%

Data Optimization for Industrial IoT-Based Recommendation Systems

et al. 2022

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The most common problems that arise when working with big data for intelligent production are analyzed in the article. The work of recommendation systems for finding the most relevant user information was considered. The features of the singular-value decomposition (SVD) and Funk SVD algorithms for reducing the dimensionality of data and providing quick recommendations were determined. An improvement of the Funk SVD algorithm using a smaller required amount of user data for analysis was proposed. According to the results of the experiments, the proposed modification improves the speed of data processing on average by 50–70% depending on the number of users and allows spending fewer computing resources. As follows, recommendations to users are provided in a shorter period and are more relevant. The faster calculation of modified Funk SVD to exchange the optimal parameters between nodes was proposed. It was determined that execution time can be reduced on average by 75% for using ten nodes exchanging the optimal decomposition parameter compared to using one. Using Spark technology for faster calculation on average by 20% compared to Hadoop was proposed. The architecture of the IIoT system was proposed, which uses a modified Funk SVD algorithm to optimize data on edge devices and monitors the effectiveness of providing recommendations using control centers and cloud resources.

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Section: Related Workmentioning

confidence: 99%

Data Optimization for Industrial IoT-Based Recommendation Systems

et al. 2022

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“…Оскільки за останні два роки багато сфер життя почали активно впроваджувати онлайн-послуги, проблема швидкого опрацювання даних є надзвичайно важливою. В медицині, освіті, фінансовій та економічній сферах слід забезпечити надійність користувацької інформації, а також якісну і швидку обробку [1][2].…”

Section: вступunclassified

Improving the efficiency of processing big data using the distributed data analysis method

Hordiychuk-Bublivsʹka¹

2021

TIT

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Various methods of collecting, storing, and analyzing information should be used to create efficient data processing systems. To solve the problem of finding the necessary information in large data sets, machine learning algorithms are used. Given that most modern large-scale information systems use a huge number of computing devices, it is much more efficient to use distributed data processing technologies. In particular, distributed machine learning is widely used, in which devices are trained on local datasets and send only results to the global model. This approach improves the reliability and confidentiality of data because user information remains on the same device. The article also presents an approach for the analysis of large amounts of information using the algorithm of Singular Value Decomposition (SVD). This algorithm allows both to reduce the amount of information, discarding redundancy, and to predict events based on the identified patterns in the data. The main features of distributed data analysis, the possibility of using complex algorithms for information analysis, and machine learning in such systems are identified. However, the algorithm of Singular Value Decomposition is quite difficult to implement given the distributed architecture. To improve the efficiency of this method in distributed systems, a special modified FedSVD algorithm is proposed. Based on this algorithm, user data is collected from different devices, but the ability to further protect them from possible interference or interception is added. The results of the work can be used in the design of systems for data analysis, increasing the reliability of the user information used, including in corporate information systems, financial or IT areas, etc. The proposed approaches can serve as a basis for the development of information technology for automatic provision of recommendations to users, prediction of emergencies in enterprises.

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“…Collaborative filtering (CF), as an important part of the recommendation system, has received extensive attention from the industry and academia [19]. The collaborative filtering algorithm uses the behavior of users similar to the user to be recommended to infer the user's preference for a specific product, and then makes corresponding recommendations based on this preference.…”

Section: Model Introductionmentioning

confidence: 99%

Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation

Wang,

Yang,

Shang

et al. 2023

cai

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This paper proposes an advanced expert collaborative filtering recommendation algorithm. Although ordinary expert system filtering algorithms have improved the recommendation accuracy of collaborative filtering technology to a certain extent, they have not screened the level of expertise of experts, and the credibility of experts varies. Therefore, this paper proposes an expert mining system based on signal fluctuations. The algorithm uses signal processing technology to filter the level of experts. This method introduces a kurtosis factor. Regarding the user's rating sequence as a random discrete signal, and then randomly sorting the user's ratings k times, the average kurtosis of the user is obtained. And take the kurtosis value as the credibility of expert users. Through experiments on multiple datasets including MovieLens, Jester, Booking-Crossings, and Last.fm, we have proved the advancement and reliability of our method.

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Recommender Systems and Collaborative Filtering

Abstract: Recommender Systems (RSs) have become an essential tool for the information society [...]

Cited by 8 publications

References 12 publications

Data Optimization for Industrial IoT-Based Recommendation Systems

Data Optimization for Industrial IoT-Based Recommendation Systems

Improving the efficiency of processing big data using the distributed data analysis method

Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation

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