Personal recommender system based on user interest community in social network model

Chen, Jianrui; Wang, Bo; Liji, U; Ouyang, Zhiping

doi:10.1016/j.physa.2019.04.197

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…Selvi et al provided "Personal recommender system based on user interest community in social network model" as a novel time weighted score matrix, in which the users and items with higher correlation are clustered into the same community by using differential equations [3]. Firstly, users' interest is calculated based on the rounding-Forgetting.…”

Section: Web Page Recommender Systemsmentioning

confidence: 99%

A novel web page recommender using data automatic clustering and Markov process

Monfared

2019

SN Appl. Sci.

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As the web expands and data grow in the networks, finding useful knowledge from the World Wide Web becomes a major challenge. As intelligent systems, the recommender systems help users to find their favorite resource among a large body of information. This research aimed at providing a new page recommender system, which enhances the accuracy of the suggested pages based on the user interest, Automatic Clustering-based Genetic Algorithm (ACGA), and all modified 3th-order Markov Model. Therefore, the frequency and page visiting time by each user have been extracted from the log file of the web server, while the user interest in the page during a session has been computed by a linear combination. ACGA involved 2 phases of the cluster based on (1) similarity measure and (2) Genetic Algorithm. The novel cluster partitioned the vectorization sessions in separate clusters through a new fitness function. Moreover, all modified 3th-order Markov Model used all orders of Markov Model simultaneously, and predicted the next page in the page recommender system. The research did these experiments on a real CTI dataset. Experimental results and their comparison with other approaches showed superiority in the accuracy of the proposed system.

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Section: Web Page Recommender Systemsmentioning

confidence: 99%

A novel web page recommender using data automatic clustering and Markov process

Monfared

2019

SN Appl. Sci.

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“…Also, they provide the facilities to enhance the adaption of applications to each user [14]. Recommender systems have been utilized in many fields, like e-commerce [15], health [16], social networks [17,18], industry [19], elearning [20], music [21], Internet of Things (IoT) [22,23], food and nutritional information system [24], and marketing [25]. They produce automation of personalization in the ecommerce environment by employing traditional and modern techniques [26] like machine learning techniques [27].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Study on the Recommender Systems in the E-Commerce

et al. 2020

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Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

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Population Learning Based Memetic Algorithm for Community Detection in Complex Networks

Sun

Cheng

et al. 2021

Data Mining and Big Data

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Personal recommender system based on user interest community in social network model

Cited by 11 publications

References 20 publications

A novel web page recommender using data automatic clustering and Markov process

A novel web page recommender using data automatic clustering and Markov process

A Systematic Study on the Recommender Systems in the E-Commerce

Population Learning Based Memetic Algorithm for Community Detection in Complex Networks

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