A Customized Non-Exclusive Clustering  Algorithm for News Recommendation Systems

Darvishy, Asghar; Ibrahim, Hamidah; Sidi, Fatimah

doi:10.29196/jubpas.v27i1.2192

Cited by 6 publications

(11 citation statements)

References 13 publications

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“…The World Wide Web has reformed the conventional model of commerce and business in which people purchase goods from the market in person [9]. Online services such as e-commerce have turned out to be the most preferred platform for shopping as the Internet provides the necessary resources to access online stores from anywhere and at any time [9,10]. Recommendation systems are designed to recommend items automatically and address the well-known issue of information overload [9,[11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…It is one of the most beneficial strategies applied over many EC platforms to attract prospective customers, convert visitors to buyers, and increase their revenue [4,[11][12][13][16][17][18][19]. Much attention has been paid to the design and development of personalized e-commerce recommendation systems that monitor and learn from the users' behavior to introduce items that best fit their preferences [9,10,14].…”

Section: Introductionmentioning

confidence: 99%

“…An analysis of the literature reveals that most of the traditional recommendation systems rely on using either data popularity or data recency or both as properties to recommend products for customers [9,11,13]. Nevertheless, in most real-world cases, this recommendation based on data popularity or data recency might not fit with the users' preferences [10,12,13,22,23]. On the other hand, some of the existing personalized e-commerce recommendation systems do not attempt to filter the number of items to recommend based on the product rate behavior of a user.…”

Section: Introductionmentioning

confidence: 99%

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OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System

et al. 2023

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The industry of e-commerce (EC) has become more popular and creates tremendous business opportunities for many firms. Modern societies are gradually shifting towards convenient online shopping as a result of the emergence of EC. The rapid growth in the volume of the data puts users in a big challenge when purchasing products that best meet their preferences. The reason for this is that people will be overwhelmed with many similar products with different brands, prices, and ratings. Consequently, they will be unable to make the best decision about what to purchase. Various studies on recommendation systems have been reported in the literature, concentrating on the issues of cold-start and data sparsity, which are among the most common challenges in recommendation systems. This study attempts to examine a new clustering technique named the Ordered Clustering-based Algorithm (OCA), with the aim of reducing the impact of the cold-start and the data sparsity problems in EC recommendation systems. A comprehensive review of data clustering techniques has been conducted, to discuss and examine these data clustering techniques. The OCA attempts to exploit the collaborative filtering strategy for e-commerce recommendation systems to cluster users based on their similarities in preferences. Several experiments have been conducted over a real-world e-commerce data set to evaluate the efficiency and the effectiveness of the proposed solution. The results of the experiments confirmed that OCA outperforms the previous approaches, achieving higher percentages of Precision (P), Recall (R), and F-measure (F).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System

et al. 2023

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“…The items with the highest similarities grouped together in one group and the items with the highest difference grouped into different groups [7,8]. Clustering operations require a large set of data to achieve high accuracy in the element prediction process and improve the clustering process [9]. To improve the accuracy of the results in this proposed system, we combine a silhouette algorithm that used to determine the best initial number of clusters.…”

Section: Introductionmentioning

confidence: 99%

Food recommendation system based on nutritional needs of human beings and user preferences

Jasim

Hamid

2022

ijhs

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Introduction: Nowadays the food types became so diverse and complicated, so human needs+ professional assistance to make his best choices especially after foods became global parameter. Food Recommendation System is a smart system that provides the best suggestions to the beneficiaries to know the best choices to their needs. Moreover, the human activities and lifestyle are affected by another types of dietaries in other foods. There is need for everybody to know what the nutrition is he/she needs. So, this research responding to these needs. The goal of the proposed system is to propose a system that provides recommendations for foods that are rich in nutritional components that people need in their daily lives based on computational model and expert preferences. Objective: The research aims to design and implement a food recommendation system has the ability to coordinate both user preferences and data clustering techniques to produce high accuracy recommendations. Material and methods: The proposed method focuses on merging computational model and user preferences to give the user the best recommended list of food options. Clustering techniques are approaches used in Recommendation system applications to group different foods according to the similarities in nutrition values.

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“…Subsequently, the University of Minnesota built and launched a movie recommendation website. In this system, users rate the movies they have watched, and then the system recommends movies with similar ratings to users based on the ratings, which is more convenient to use [12]. In addition, a laboratory developed the music recommendation system Ringo, which requires users to compare the ratings of musicians, calculate the similarity between users based on the results of the ratings, and cluster users with higher similarities together [13].…”

Section: Introductionmentioning

confidence: 99%

Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

Han

2020

Complexity

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Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.

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A Customized Non-Exclusive Clustering Algorithm for News Recommendation Systems

Cited by 6 publications

References 13 publications

OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System

OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System

Food recommendation system based on nutritional needs of human beings and user preferences

Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

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