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  • Open Access


    Movie Recommendation Algorithm Based on Ensemble Learning

    Wei Fang1,2,*, Yu Sha1, Meihan Qi1, Victor S. Sheng3

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 609-622, 2022, DOI:10.32604/iasc.2022.027067

    Abstract With the rapid development of personalized services, major websites have launched a recommendation module in recent years. This module will recommend information you are interested in based on your viewing history and other information, thereby improving the economic benefits of the website and increasing the number of users. This paper has introduced content-based recommendation algorithm, K-Nearest Neighbor (KNN)-based collaborative filtering (CF) algorithm and singular value decomposition-based (SVD) collaborative filtering algorithm. However, the mentioned recommendation algorithms all recommend for a certain aspect, and do not realize the recommendation of specific movies input by specific users which will cause the recommended content… More >

  • Open Access


    A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations

    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, implicit similarity, similarity transitivity and… More >

  • Open Access


    Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

    Saurabh Pal1, Pijush Kanti Dutta Pramanik1, Musleh Alsulami2, Anand Nayyar3,*, Mohammad Zarour4, Prasenjit Choudhury1

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3981-4001, 2021, DOI:10.32604/cmc.2021.017966

    Abstract With the popularity of e-learning, personalization and ubiquity have become important aspects of online learning. To make learning more personalized and ubiquitous, we propose a learner model for a query-based personalized learning recommendation system. Several contextual attributes characterize a learner, but considering all of them is costly for a ubiquitous learning system. In this paper, a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling. A total of 208 students are surveyed. DEMATEL (Decision Making Trial and Evaluation Laboratory) technique is used to establish the validity and importance of the identified contexts and find… More >

  • Open Access


    Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

    Tao Li1, Qi Qian2, Yongjun Ren3,*, Yongzhen Ren4, Jinyue Xia5

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 779-791, 2021, DOI:10.32604/cmc.2020.010424

    Abstract The application field of the Internet of Things (IoT) involves all aspects, and its application in the fields of industry, agriculture, environment, transportation, logistics, security and other infrastructure has effectively promoted the intelligent development of these aspects. Although the IoT has gradually grown in recent years, there are still many problems that need to be overcome in terms of technology, management, cost, policy, and security. We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data. To avoid the leakage and loss of various user data, this paper developed a hybrid algorithm of… More >

  • Open Access


    Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

    Yong Yu1, Yongjun Luo1, Tong Li2, Shudong Li3, *, Xiaobo Wu4, Jinzhuo Liu1, *, Yu Jiang3, *

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 489-507, 2020, DOI:10.32604/cmc.2020.07616

    Abstract Personalized recommendation algorithms, which are effective means to solve information overload, are popular topics in current research. In this paper, a recommender system combining popularity and novelty (RSCPN) based on one-mode projection of weighted bipartite network is proposed. The edge between a user and item is weighted with the item’s rating, and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users. RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in… More >

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