User similarity calculation method

Abstract

The invention provides a user similarity calculation method, which comprises the steps of S1, performing clustering grouping according to user attributes, and calculating similarity simattr based on static attributes; S2, calculating the similarity simtime according to the fact that the user similarity is in direct proportion to the login time but is subjected to the time attenuation effect; S3, calculating the similarity simfre according to the negative correlation relationship between the user similarity and the operation behavior frequency of the user; s4, according to the uncertainty thatthe user is attracted by the product, the self-information amount is increased for the user, and the similarity siminf is obtained; and S5, superposing and normalizing the similarities calculated in the steps S1-S4 to obtain the final user similarity.

Description

technical field [0001] The present invention relates to recommendation algorithm technology, in particular to a method for calculating user similarity Background technique [0002] In the traditional collaborative filtering recommendation algorithm, the calculation of user similarity is relatively simple, and the similarity is generally judged directly according to the user's historical behavior, so the accuracy is not high. [0003] For this reason, the prior art once proposed a "Collaborative Filtering Recommendation Algorithm Integrating Penalty Factor and Time Weight", DOI is 10.19358 / j.issn.2096-5133.2020.05.004. But there are some problems in the traditional collaborative filtering algorithm, such as cold start, data sparseness and Matthew effect. Lan Yan et al. used the attenuation factor to establish a non-linear time weighting function, and gave different time weights to the score, which improved the accuracy of the recommendation. Although the above literature ta...

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Problems solved by technology

However, it only considers the positive impact of clustering on similarity, and does not consider the influence of other factors that affect similarity, such as user frequency, time difference of operation behavior, and location distance, on similarity.

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