Recommendation system techniques and related issues: a survey

Kumar, Pushpendra; Thakur, Ramjeevan Singh

doi:10.1007/s41870-018-0138-8

Cited by 90 publications

(30 citation statements)

References 21 publications

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“…On the one hand, the increasing amount of product data leads to the untimely data processing of recommendation system, and users cannot quickly and accurately search for the products they want [5]; on the other hand, the user's demand becomes more and more diversified, so that the recommendation system cannot recommend the products that users are potentially interested in, and the recommendation content is not diversified enough [6]. With the wide use of e-commerce recommendation system in various websites, a large number of user' browsing records and purchase records have been accumulated in the database.…”

Section: Introductionmentioning

confidence: 99%

The application of e-commerce recommendation system in smart cities based on big data and cloud computing

Zhang

2021

ComSIS

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In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.

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Section: Introductionmentioning

confidence: 99%

The application of e-commerce recommendation system in smart cities based on big data and cloud computing

Zhang

2021

ComSIS

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“…Recommendation system (RS) is a branch of information filtering systems, which can find out connections between users and items [1], and is widely used in mobile applications, e-commerce, and even robotics. Specifically, it seeks to predict the rating or preference that a user would give to an item.…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Multi-Task Framework for Knowledge Graph Enhanced Recommendation

Zhou

Zhang

et al. 2020

IEEE Access

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Multi-task learning (MTL) is a learning paradigm which can improve generalization performance by transferring knowledge among multiple tasks. Traditional collaborative filtering recommendation methods suffer from cold start, sparsity and scalability problems. The latest research has shown that applying side information of knowledge graph can not only solve the problems above, but also improve the accuracy of recommendation. However, existing multi-task methods for knowledge graph enhanced recommendation expose obvious issues of disclosing the private information of training samples. In order to solve these problems, we put forward a privacy-preserving multi-task framework for knowledge graph enhanced recommendation. In specific, Laplacian noise is added into the recommendation module to guarantee the privacy of sensitive data and knowledge graph is utilized to improve the accuracy of recommendation. Extensive experimental results on three datasets demonstrate that the proposed method can not only preserve the privacy of sensitive training data, but also have little effect on the prediction accuracy of the model.

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“…To achieve a user-interactive advertisement service, it is necessary to introduce technologies that are able to predict user preferences or characteristics. The recommendation system is a high-quality technology that predicts a user's preference based on various factors, such as search history, evaluation score, and frequency of use [5]. Therefore, it can be implemented in the advertisement domain.…”

Section: Introductionmentioning

confidence: 99%

“…(3)(4)(5)(6) [41]. True positive (TP) means a case where a high preference item is recommended, and true negative (TN) means a case where a low preference item is not recommended.…”

mentioning

confidence: 99%

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

2020

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The viewing time of media content per week through TV is still dominant. Users are exposed to numerous advertisements, such as commercials, electronic home shopping, product placement (PPL), and T-Commerce while watching TV programs. Most of the advertisement systems provide a good overview of products. However, traditional advertising services do not consider user preferences, meaning it is difficult to expect anything more than mere exposure to them. We can adopt a recommendation system to predict the preference. However, existing recommendation systems find it difficult to satisfy the realtime requirements of online broadcasting because of the large overhead incurred in preference prediction processes. In this paper, we propose a real-time recommendation system to provide personalized advertisements. The proposed system generates tree models based on user historical data. To reduce the overhead of preference prediction, we introduce a sorted HashMap that enables fast tree searches. For sophisticated preference prediction, the proposed system normalizes the users' preferences by considering the characteristics of their tree model. Finally, we conduct experiments to evaluate the performance of the proposed tree-based recommendation system.

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Recommendation system techniques and related issues: a survey

Cited by 90 publications

References 21 publications

The application of e-commerce recommendation system in smart cities based on big data and cloud computing

The application of e-commerce recommendation system in smart cities based on big data and cloud computing

A Privacy-Preserving Multi-Task Framework for Knowledge Graph Enhanced Recommendation

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

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