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  • Open Access


    A Novel Service Recommendation Approach in Mashup Creation

    Yanmei Zhang1, Xiao Geng2, Shuiguang Deng3

    Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 513-525, 2019, DOI:10.31209/2019.100000108

    Abstract With the development of service computing technologies, the online services are massive and disordered now. How to find appropriate services quickly and build a more powerful composed service according to user interests has been a research focus in recent years. Current service recommendation algorithms often directly follow the traditional recommendation framework of ecommerce, which cannot effectively assist users to complete dynamic online business construction. Therefore, a novel service recommendation approach named UISCS (User-Interest- initial Services-Correlation-successor Services) is proposed, which is designed for interactive scenario of service composition, and it mines the user implicit interests and the service correlations for service… More >

  • Open Access


    Context-Aware Collaborative Filtering Framework for Rating Prediction Based on Novel Similarity Estimation

    Waqar Ali1, 2, Salah Ud Din1, Abdullah Aman Khan1, Saifullah Tumrani1, Xiaochen Wang1, Jie Shao1, 3, *

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1065-1078, 2020, DOI:10.32604/cmc.2020.010017

    Abstract Recommender systems are rapidly transforming the digital world into intelligent information hubs. The valuable context information associated with the users’ prior transactions has played a vital role in determining the user preferences for items or rating prediction. It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades. This paper presents a novel Context Based Rating Prediction (CBRP) model with a unique similarity scoring estimation method. The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential… 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


    Geek Talents: Who are the Top Experts on GitHub and Stack Overflow?

    Yijun Tian 1, *, Waii Ng1, Jialiang Cao1, Suzanne McIntosh1

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 465-479, 2019, DOI:10.32604/cmc.2019.07818

    Abstract In the field of Computer Science, software developers need to use a wide array of social collaborative platforms for learning and cooperating. The most popular ones are GitHub and Stack Overflow. Existing platforms only support search queries to extract relevant repository information from GitHub, or questions and answers from Stack Overflow. This ignores the valuable coder-related part-who are the top experts (geek talents) in a specific area? This information is important to companies, open source projects, and to those who want to learn from an expert role model. Thus, how to find the right developers is quite a crucial yet… More >

  • Open Access


    Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering

    Mengwei Hou1, Rong Wei1,*, Tiangang Wang1, Yu Cheng2, Buyue Qian3

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 137-149, 2018, DOI: 10.3970/cmc.2018.02438

    Abstract Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect patients’ treatment information and demographic… 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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