Hybrid clustering recommendation method based on Gaussian distribution and distance similarity

Abstract

The invention discloses a hybrid clustering recommendation method based on Gaussian distribution and distance similarity. The hybrid clustering recommendation method includes the following steps: S1, acquiring a user behavior data set, dividing the behavior data set into labeled data and no-label data, and performing distance metric learning on the labeled data and the no-label data respectively; S2, according to a distance metric weight matrix and a Gaussian hybrid model, performing hybrid calculation to acquire a target function, and performing optimized solving on the target function; S3, after acquiring an optimized solution of the target function, acquiring clustering behavior data through a clustering algorithm, and recommending clustering behavior data to users.

Description

technical field [0001] The invention relates to the field of computer data mining, in particular to a recommendation method based on Gaussian distribution and hybrid clustering of distance similarity. Background technique [0002] The recommendation method based on collaborative filtering uses the similarity of user's interest preferences to generate recommendations, which is to recommend items that similar users like to target users. Its strategy is that users with the same or similar values, knowledge levels or interest preferences have similar needs for information. A significant advantage of the collaborative filtering recommendation method is that there are no special requirements for the recommended objects, and it can recommend items that are difficult to analyze such as artwork, music, and movies. Such as figure 1 shown. [0003] The key to the recommendation method based on collaborative filtering lies in the calculation of user similarity. Commonly used similari...

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Problems solved by technology

[0004] ① There is a single way to calculate the similarity of user interests;

[0005] ②The time complexity of the algorithm grows quadratically with the number of users (that is, O(|U|*|U|)), which is very time-consuming when the number of users is large;

[0006] ③ These similarity calculation methods cannot mine the potential hidden constraints of user behavior data, and in many cases, these potential hidden constraints (pair constraints of must-link and cannot-link) do exist;

[0007] ④ It is not easy to use user tag information

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