A recommendation method integrated with a curiosity mechanism of a user
A recommendation method and curiosity technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as user resistance
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[0060] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.
[0061] A recommendation method that incorporates user curiosity mechanisms, such as figure 1 shown, including the following steps:
[0062] Step 1: In the user data without friend relationship, use the k-nearest neighbor algorithm to find implicit friends. In the historical data of each user, for implicit or explicit friends, use the Pearson correlation coefficient to calculate the user and friend The similarity between each user's friends is sorted from large to small according to the similarity.
[0063] Each user data can be composed of (u, i, r, t) tuples, that is, user u scores r for item i at time t, and U and I refer to the collection of users and items respectively. |U|=m, |I|=n represent the number of users and items in the data set, respectively.
[0064] St...
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