Abstract

The rapid development of computer science and internet technology has enabled the prevailing digital transformation. Nowadays, more and more service companies have built up their smart service platforms. Due to the rapid growth in services, consumers may not get their personalized needs accurately. So companies have concentrated more on smart services than before, such as proactive recommending services. However, few studies focus on smart service recommendation, and few recommendation methods are suitable for service recommending. This paper proposes a smart service recommendation method based on user dynamic portrait and collaborative filtering. Theoretically, this method improves the accuracy of recommendation and reflects the change of users’ preference. Finally, we use the users’ data from a science and technology service platform to verify the effectiveness of the method.

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