Data processing method, device and equipment

Abstract

The embodiment of the invention discloses a data processing method, device and equipment, and belongs to the field of cloud technology-artificial intelligence service. The method comprises the following steps: ; obtaining sample data sets, wherein the sample data set comprises sample interestingness of at least two sample objects and distinguishing parameters for distinguishing the at least two sample objects; adopting a recommendation model to extract prediction attribute feature vectors of at least two sample objects, predicting and identifying according to the prediction attribute feature vector to obtain prediction interestingness of at least two sample objects; adjusting the recommendation model according to the prediction attribute feature vectors, the distinguishing parameters, theprediction interestingness and the sample interestingness; and taking the adjusted recommendation model as a target recommendation model, wherein the distinguishing degree between the updated prediction attribute feature vectors output by the target recommendation model for the at least two sample objects is matched with the distinguishing degree corresponding to the distinguishing parameter. Through the embodiment of the invention, the distinction degree of the recommendation model to the object can be improved, and accurate recommendation can be realized.

Description

technical field [0001] The present application relates to the field of cloud technology-artificial intelligence services, and in particular to a data processing method, device and equipment. Background technique [0002] With the development of the Internet and e-commerce, more and more merchants use online sales to sell goods, that is, merchants publish relevant information (such as the type and price of goods) on the website, and users purchase through the website. desired product. However, there are usually a large number of products on the website, and it takes a long time for users to find the products they want. Based on this, at present, products are mainly recommended for users based on the purchase behavior of users, so as to help users quickly find the products they want to buy. . For example, if user A has purchased beer and diapers on the website, the recommendation terminal determines that the beer and diapers are similar, and recommends the beer and diapers t...

AI Technical Summary

Problems solved by technology

However, the points of interest of different users are different, that is, the products that different users want to buy are inconsistent. Therefore, the accuracy of the product recommendation method based on user purchase behavior is relatively low.

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