Xiangyu Wu¹, Xuehui Du^1,*, Qiantao Yang^1,2, Aodi Liu¹, Na Wang¹, Wenjuan Wang¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1491-1519, 2024, DOI:10.32604/cmc.2024.052708 - 18 July 2024

Abstract Blockchain has been widely used in finance, the Internet of Things (IoT), supply chains, and other scenarios as a revolutionary technology. Consensus protocol plays a vital role in blockchain, which helps all participants to maintain the storage state consistently. However, with the improvement of network environment complexity and system scale, blockchain development is limited by the performance, security, and scalability of the consensus protocol. To address this problem, this paper introduces the collaborative filtering mechanism commonly used in the recommendation system into the Practical Byzantine Fault Tolerance (PBFT) and proposes a Byzantine fault-tolerant (BFT) consensus… More >

Weiming Huang^1,2, Baisong Liu^1,*, Zhaoliang Wang¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4449-4469, 2024, DOI:10.32604/cmc.2024.050389 - 20 June 2024

Abstract In the tag recommendation task on academic platforms, existing methods disregard users’ customized preferences in favor of extracting tags based just on the content of the articles. Besides, it uses co-occurrence techniques and tries to combine nodes’ textual content for modelling. They still do not, however, directly simulate many interactions in network learning. In order to address these issues, we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations. Specifically, we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles… More >

S. Abinaya^*, K. Uttej Kumar

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2269-2286, 2024, DOI:10.32604/cmc.2024.047167 - 27 February 2024

Abstract A Recommender System (RS) is a crucial part of several firms, particularly those involved in e-commerce. In conventional RS, a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences. Nowadays, businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’ preferences. On the other hand, the collaborative filtering (CF) algorithm utilizing AutoEncoder (AE) is seen to be effective in identifying user-interested items. However, the cost of these computations increases nonlinearly as the number of items and users… More >

Muhammad Ibrahim¹, Imran Sarwar Bajwa¹, Nadeem Sarwar^2,*, Haroon Abdul Waheed³, Muhammad Zulkifl Hasan⁴, Muhammad Zunnurain Hussain⁴

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5301-5317, 2023, DOI:10.32604/cmc.2023.032856 - 28 December 2022

Abstract Recommendation services become an essential and hot research topic for researchers nowadays. Social data such as Reviews play an important role in the recommendation of the products. Improvement was achieved by deep learning approaches for capturing user and product information from a short text. However, such previously used approaches do not fairly and efficiently incorporate users’ preferences and product characteristics. The proposed novel Hybrid Deep Collaborative Filtering (HDCF) model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations. To overcome the cold start problem, the new overall rating is generated… More >

P. N. Ramesh^1,*, S. Kannimuthu²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3845-3860, 2023, DOI:10.32604/iasc.2023.031329 - 17 August 2022

Abstract The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system… More >

S. Rajalakshmi^1,*, K. R. Santha²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1251-1262, 2023, DOI:10.32604/csse.2023.024036 - 15 June 2022

Abstract A recommender system is an approach performed by e-commerce for increasing smooth users’ experience. Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions. This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-commerce. This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system. The feature selection's objective is to pick a feature subset having the least feature similarity as well as highest… More >

Donglei Lu¹, Dongjie Zhu^2,*, Haiwen Du³, Yundong Sun³, Yansong Wang², Xiaofang Li⁴, Rongning Qu⁴, Ning Cao¹, Russell Higgs⁵

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1133-1146, 2022, DOI:10.32604/csse.2022.021525 - 08 February 2022

Abstract The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to the user based on the known historical interaction data of the target user. Furthermore, the combination of the recommended algorithm based on collaborative filtration and other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model (CoFM) is one representative research. CoFM, a fusion recommendation model combining the collaborative filtering model FM and the graph embedding model TransE, introduces the information of many entities and their relations in the… More >

Adel Binbusayyis^*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 501-513, 2022, DOI:10.32604/iasc.2022.022239 - 05 January 2022

Abstract The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from… More >

Pradeep Kumar Singh¹, Pijush Kanti Dutta Pramanik¹, Madhumita Sardar¹, Anand Nayyar^2,3,*, Mehedi Masud⁴, Prasenjit Choudhury¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2827-2846, 2022, DOI:10.32604/cmc.2022.020437 - 07 December 2021

Abstract A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing. To keep the recommendation systems reliable, authentic, and superior, the security of these systems is very crucial. Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks, in this paper, we prove that they fail to detect a new or unknown attack. We develop a new attack model, named Obscure attack, with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended. The More >

G. Parthasarathy^1,*, S. Sathiya Devi²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2419-2434, 2022, DOI:10.32604/cmc.2022.019805 - 07 December 2021

Abstract Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In… More >