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  • Open Access


    Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

    Chao Ma1,*, Yinggang Sun1, Zhenguo Yang1, Hai Huang1, Dongyang Zhan2,3, Jiaxing Qu4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6201-6217, 2022, DOI:10.32604/cmc.2022.022717

    Abstract The number of mobile application services is showing an explosive growth trend, which makes it difficult for users to determine which ones are of interest. Especially, the new mobile application services are emerge continuously, most of them have not be rated when they need to be recommended to users. This is the typical problem of cold start in the field of collaborative filtering recommendation. This problem may makes it difficult for users to locate and acquire the services that they actually want, and the accuracy and novelty of service recommendations are also difficult to satisfy users. To solve this problem,… More >

  • 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


    Dynamic Trust Model Based on Service Recommendation in Big Data

    Gang Wang1,*, Mengjuan Liu2

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 845-857, 2019, DOI:10.32604/cmc.2019.03678

    Abstract In big data of business service or transaction, it is impossible to provide entire information to both of services from cyber system, so some service providers made use of maliciously services to get more interests. Trust management is an effective solution to deal with these malicious actions. This paper gave a trust computing model based on service-recommendation in big data. This model takes into account difference of recommendation trust between familiar node and stranger node. Thus, to ensure accuracy of recommending trust computing, paper proposed a fine-granularity similarity computing method based on the similarity of service concept domain ontology. This… More >

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