A Neural Networks-Based Clustering Collaborative Filtering Algorithm in E-Commerce Recommendation System

Mai, Jianying; Fan, Yongjian; Shen, Y.

doi:10.1109/wism.2009.129

Cited by 21 publications

(6 citation statements)

References 1 publication

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“…While they fill the vacant ratings using BP neural networks, they form nearest neighborhood using item based CF. Mai et al [35] focus on e-commerce recommendation system and propose a neural networks-based clustering CF algorithm with the aim of overcoming sparsity problem and getting more effective and the more accurate recommendation results. Almaghrabi and Chetty [36] propose a new deep learning-based framework with the aim of capturing the deep and hidden interactions between users and items and improving the performance of recommender system.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

Evolutionary neural networks for improving the prediction performance of recommender systems

Seref¹,

Bostancı²,

Güzel³

2021

Turk J Elec Eng & Comp Sci

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Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving performance of them. In this study, prediction performances of neural networks were evaluated and their performances were improved using genetic algorithms. Performance of this study was compared with other studies, after that, superiority of this study was shown. While multilayer perceptron, generalized feed-forward network and CANFIS (Coactive Neuro Fuzzy Inference Systems) were used as a neural network algorithms, Movielens 100K and Movielens 1M datasets which are preferred in recommender systems studies mostly were used to train and test the system. Mean square error and root mean square error were employed as performance metrics. As a result, it was observed that genetic algorithm improves performance of neural networks and prediction performance of hybrid combination of neural networks and genetic algorithm is superior to prediction performance of recommender systems available in the literature.

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Section: Literature Review and Backgroundmentioning

confidence: 99%

Evolutionary neural networks for improving the prediction performance of recommender systems

Seref¹,

Bostancı²,

Güzel³

2021

Turk J Elec Eng & Comp Sci

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“…CF, one of the most popular and widely implemented methods in RSs, is a technique utilized for linking the data of an applier with the data of other alike appliers depending on buying patterns to produce directions for the user for prospective shopping [71,75,85,[107][108][109][110][111][112][113][114]. Amazon uses CF techniques for making its recommendations depending on a client's previous buying and buying of those that bought the same products.…”

Section: Overview Of Collaborative Filtering Mechanismmentioning

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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“…The clustering process begins by checking the data with the function similarity, characteristics similarity and description similarity and then the advanced Agglomerative Clustering algorithm is used for finding the similarities between words with respect to the measured factor, once the cluster of data is formed this will be act as wrappers to give out the output for the user query. It is going to give exact information data for the obtained process with advanced mining process with the automated collaborative filter which in turn will be acting wrapper to give out the specified output data which is a mined content information [10]. Then the obtained information will pass on to the web with the snippets then before giving out the information the agent will supervise the information and form the list for the data given along with the ranking the listed data will be moved to the data repository and from the repository the information will be given to the user based on their priority.…”

Section: Overview Of the Proposed Workmentioning

confidence: 99%

“…An automated wrapper will do the desired functionality for framing the data format for the given request and the advanced meta crawler will facilitate to provide the information desirably with the web data format within the time stipulation. The exact content and remaining hierarchy of data will be moved to the repository so that any change in the information can be updated and used for the future purpose [9] [10].…”

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confidence: 99%

An Approach for Content Retrieval from Web Pages Using Clustering Techniques

Manjula¹,

Chilambuchelvan²

2016

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Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is not an appropriate, because it gives ambiguous information for the user query, and the user cannot get relevant information within the stipulated time. To overcome these issues, we propose a new methodology for information retrieval EPCRR by providing the top most exact information to the user, by using the collaborative clustered automated filter which makes use of the collaborative data set and filter works on the prediction by providing the highest ranking for the exact data retrieved. The retrieval works on the basis of recommendation of data which consists of relevant data set with highest priority from the cluster of data which is on high usage. In this work, we make use of the automated wrapper which works similar to the meta crawler functionality and it obtains the content in the semantic usage data format. Obtained information from the user to the agent will be ranked based on the Enabled Pile clustered data with respect to the metadata information from the agent and end-user. The information is given to the end-user with the top most ranking data within the stipulated time and the remaining top information will be moved to the data repository for future use. The data collected will remain stable based on the user preference and works on the intelligence system approach in which the user can choose any information under any instances and can be provided with suitable high range of exact content. In this approach, we find that the proposed algorithm has produced better results than existing work and it costs less online computation time.

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A Neural Networks-Based Clustering Collaborative Filtering Algorithm in E-Commerce Recommendation System

Cited by 21 publications

References 1 publication

Evolutionary neural networks for improving the prediction performance of recommender systems

Evolutionary neural networks for improving the prediction performance of recommender systems

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

An Approach for Content Retrieval from Web Pages Using Clustering Techniques

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