Yingchao Wang¹, Yuanhao Zhu¹, Zongtian Zhang¹, Huihuang Liu^1,* , Peng Guo²

Journal of New Media, Vol.3, No.4, pp. 119-128, 2021, DOI:10.32604/jnm.2021.016655

Abstract In order to improve user satisfaction and loyalty on e-commerce websites, recommendation algorithms are used to recommend products that may be of interest to users. Therefore, the accuracy of the recommendation algorithm is a primary issue. So far, there are three mainstream recommendation algorithms, content-based recommendation algorithms, collaborative filtering algorithms and hybrid recommendation algorithms. Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings. The contentbased recommendation algorithm has the problem of the diversity of recommended items, while the collaborative filtering algorithm has the problem of data sparsity and scalability. On the basis of More >

M. Arun Manicka Raja^1,*, S. Swamynathan²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1263-1280, 2022, DOI:10.32604/cmc.2022.019451

Abstract News feed is one of the potential information providing sources which give updates on various topics of different domains. These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources. In this paper, the news summarization system is proposed for the news data streams from RSS feeds and Google news. Since news stream analysis requires live content, the news data are continuously collected for our experimentation. The major contributions of this work involve domain corpus based… More >

Abdelrahman H. Hussein, Qasem M. Kharma, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 661-675, 2022, DOI:10.32604/iasc.2022.020132

Abstract Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, More >

Qusai Y. Shambour^*, Abdelrahman H. Hussein, Qasem M. Kharma, Mosleh M. Abualhaj

Computer Systems Science and Engineering, Vol.40, No.1, pp. 113-125, 2022, DOI:10.32604/csse.2022.017221

Abstract Requirements engineering (RE) is among the most valuable and critical processes in software development. The quality of this process significantly affects the success of a software project. An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources. Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements. However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately. Recommender systems have been widely recognized as an effective solution to such problem. Accordingly, this study proposes More >

Mohanad Al-Ghobari¹, Amgad Muneer^2,*, Suliman Mohamed Fati³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1553-1570, 2021, DOI:10.32604/cmc.2021.016348

Abstract Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites, accommodation, and food according to their interests. This objective makes it harder for tourists to decide and plan where to go and what to do. Aside from hiring a local guide, an option which is beyond most travelers’ budgets, the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews. Therefore, this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue. Accordingly,… More >

Zejun Yang¹, Denghui Xia¹, Jin Liu¹, Chao Zheng², Yanzhen Qu^1,3,4, Yadang Chen¹, Chengjun Zhang^1,2,3,*

Journal on Internet of Things, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jiot.2021.015401

Abstract Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify More >

Thavavel Vaiyapuri^*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 487-503, 2021, DOI:10.32604/cmc.2021.015998

Abstract The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating… More >

Jie Zhang^1,5, Juan Yang^2,*, Li Wang³, Yizhang Jiang⁴, Pengjiang Qian⁴, Yuan Liu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1275-1291, 2021, DOI:10.32604/cmes.2021.012112

Abstract With the rapid development of the Internet, the amount of data recorded on the Internet has increased dramatically. It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data. In this study, we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce. This study has two main innovations. First, we propose a mechanism that embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item. Second, we propose a novel More >

Chongchao Cai^{1, 2}, Huahu Xu^{1, *}, Jie Wan², Baiqing Zhou², Xiongwei Xie³

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2475-2488, 2020, DOI:10.32604/cmc.2020.011693

Abstract In social networks, user attention affects the user’s decision-making, resulting in a performance alteration of the recommendation systems. Existing systems make recommendations mainly according to users’ preferences with a particular focus on items. However, the significance of users’ attention and the difference in the influence of different users and items are often ignored. Thus, this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social networks. We first constructed the basic user and item matrix via convolutional neural networks (CNN). Then, we obtained user preferences by using the relationships between users More >

C. Raja Kumar¹, VE. Jayanthi²

Computer Systems Science and Engineering, Vol.34, No.3, pp. 123-129, 2019, DOI:10.32604/csse.2019.34.123

Abstract Collaborative Filtering (CF) is one of the popular methodology in recommender systems. It suffers from the data sparsity problem, recommendation inaccuracy and big-error in predictions. In this paper, the efficient advisory tool is implemented for the younger generation to choose their right career based on their knowledge. It acquires the notions of indiscernible relation from Fuzzy Rough Sets Theory (FRST) and propose a novel algorithm named as Fuzzy Rough Set Theory Based Collaborative Filtering Algorithm (FRSTBCF). To evaluate the model, data is prepared using the cross validation method. Based on that, ratings are evaluated by… More >