Collaborative Filtering Recommendation Algorithm for Heterogeneous Data Mining in the Internet of Things

With the popularization of Internet of Things (IOT) technology, a large number of multi-source heterogeneous data are constantly generated and collected by cloud platforms, which indicates that the problem of large data in IOT has become increasingly prominent, especially for massive tags and information in IOT which is urgent to use appropriate data mining algorithms to mine the value of these data. A collaborative filtering recommendation algorithm based on multi-information source fusion (CFR-MIF) is proposed where a feature vector and time weight function are introduced to improve the accuracy of top-N recommendation. It can conveniently and effectively process the IoT data, and furthermore integrate, manage and store the massive data collected from different industries and data formats. Besides, It also provides data mining services in the whole IoT realizing prediction and decision-making, which reverses control these sensor networks, so as to control the movement and development process of objective in the Internet of Things. The experimental results based on DeviceLens 1M data set show that the proposed algorithm greatly improves the accuracy of recommendation results, recall rate and F1 value compared with other advanced algorithms.