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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


    Cold Start Problem of Vehicle Model Recognition under Cross-Scenario Based on Transfer Learning

    Hongbo Wang1, *, Qian Xue1, Tong Cui1, Yangyang Li2, Huacheng Zeng3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 337-351, 2020, DOI:10.32604/cmc.2020.07290

    Abstract As a major function of smart transportation in smart cities, vehicle model recognition plays an important role in intelligent transportation. Due to the difference among different vehicle models recognition datasets, the accuracy of network model training in one scene will be greatly reduced in another one. However, if you don’t have a lot of vehicle model datasets for the current scene, you cannot properly train a model. To address this problem, we study the problem of cold start of vehicle model recognition under cross-scenario. Under the condition of small amount of datasets, combined with the method of transfer learning, load… More >

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