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  • Open Access


    Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

    Yang Li, Fei Yin, Xianghui Hui*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4047-4063, 2023, DOI:10.32604/cmc.2023.036945

    Abstract With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people’s livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a recommendation algorithm based on the… More >

  • Open Access


    Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

    R. Sujatha1,*, T. Abirami2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031552

    Abstract The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A substantial trade-off is applied in… More >

  • Open Access


    Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

    Weihua Liu1, Haoyang Wan2,*, Boyuan Yan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 239-258, 2023, DOI:10.32604/cmes.2022.022827

    Abstract With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists. Browsing short-form mobile video in fragmented time has become the mainstream of user’s life. Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements. Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users’ points of interest on one hand effectively. On the other hand, integrating user preferences and the content of items urgently intensify the requirements for platform… More > Graphic Abstract

    Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

  • 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


    Design of Hybrid Recommendation Algorithm in Online Shopping System

    Yingchao Wang1, Yuanhao Zhu1, Zongtian Zhang1, Huihuang Liu1,* , 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 these two algorithms, the hybrid… More >

  • Open Access


    Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

    Zejun Yang1, Denghui Xia1, Jin Liu1, Chao Zheng2, Yanzhen Qu1,3,4, Yadang Chen1, Chengjun Zhang1,2,3,*

    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 the recommendation score to make… More >

  • Open Access


    Recommendation Algorithm Based on Probabilistic Matrix Factorization with Adaboost

    Hongtao Bai1, 2, Xuan Li1, 2, Lili He1, 2, Longhai Jin1, 2, Chong Wang1, 2, 3, Yu Jiang1, 2, *

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1591-1603, 2020, DOI:10.32604/cmc.2020.09981

    Abstract A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements, taking into account individual characteristics, such as body weight with individual health conditions, such as diabetes. Current dietary recommendations employ association rules, content-based collaborative filtering, and constraint-based methods, which have several limitations. These limitations are due to the existence of a special user group and an imbalance of non-simple attributes. Making use of traditional dietary recommendation algorithm researches, we combine the Adaboost classifier with probabilistic matrix factorization. We present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost. A… More >

  • Open Access


    A New Time-Aware Collaborative Filtering Intelligent Recommendation System

    Weijin Jiang1,2,3, Jiahui Chen1,*, Yirong Jiang4,*, Yuhui Xu1, Yang Wang1, Lina Tan1, Guo Liang5

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 849-859, 2019, DOI:10.32604/cmc.2019.05932

    Abstract Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,… More >

  • Open Access


    Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network

    Sheng Bin1,*, Gengxin Sun1, Ning Cao2, Jinming Qiu2, Zhiyong Zheng3, Guohua Yang4, Hongyan Zhao5, Meng Jiang6, Lina Xu7

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 659-674, 2019, DOI:10.32604/cmc.2019.05858

    Abstract Recommendation system is one of the most common applications in the field of big data. The traditional collaborative filtering recommendation algorithm is directly based on user-item rating matrix. However, when there are huge amounts of user and commodities data, the efficiency of the algorithm will be significantly reduced. Aiming at the problem, a collaborative filtering recommendation algorithm based on multi-relational social networks is proposed. The algorithm divides the multi-relational social networks based on the multi-subnet complex network model into communities by using information dissemination method, which divides the users with similar degree into a community. Then the user-item rating matrix… More >

  • Open Access


    An Entity-Association-Based Matrix Factorization Recommendation Algorithm

    Gongshen Liu1, Kui Meng1,*, Jiachen Ding1, Jan P. Nees1, Hongyi Guo1, Xuewen Zhang1

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 101-120, 2019, DOI:10.32604/cmc.2019.03898

    Abstract Collaborative filtering is the most popular approach when building recommender systems, but the large scale and sparse data of the user-item matrix seriously affect the recommendation results. Recent research shows the user’s social relations information can improve the quality of recommendation. However, most of the current social recommendation algorithms only consider the user's direct social relations, while ignoring potential users’ interest preference and group clustering information. Moreover, project attribute is also important in item rating. We propose a recommendation algorithm which using matrix factorization technology to fuse user information and project information together. We first detect the community structure using… More >

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