Cold start recommendation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a cold start recommendation method, which comprises the following steps of: obtaining a text vector of each article under a first article category label, and classifying the text vectors through a pre-trained classification neural network to obtain first article category information of each article; obtaining a preset first mapping relation between the user group and the second article category label, and constructing a second mapping relation between the second article category label and the user group according to the first mapping relation; according to a second mapping relation, mapping the first article category information of each article into a user group, and counting the occurrence frequency of each user group corresponding to the first article category information of each article; and marking a first article category label on the corresponding user group based on the occurrence frequency, and recommending articles to the corresponding user group according to the first article category label and the second article category label. According to the invention, the cold start of the recommendation system can be realized, and the cold start recommendation accuracy can be improved.

Description

technical field [0001] The invention relates to the field of big data, and in particular, to a cold start recommendation method, device, electronic device and storage medium. Background technique [0002] With the development of big data technology, big data can be used to mine potential users and help users reduce decision-making costs, such as recommendation systems, which can predict users' future behaviors and interests based on users' historical behavior data, so a large number of user behaviors Data has become an important part and prerequisite for recommender systems. It can be said that recommender systems are driven based on a large amount of user behavior data and item data. However, for many applications or websites that have just started recommending systems, there is not enough user behavior data and item data. The recommendation accuracy of the recommendation system designed without a large amount of user behavior data is low, and it is easy for users to The r...

AI Technical Summary

Problems solved by technology

However, for many applications or websites that are just starting to make recommendation systems, there is not enough user behavior data and item data. The recommendation accuracy of the recommendation system designed without a large amount of user behavior data is low, and it is easy for users to be confused. The recommendation results are dissatisfied, so they are unwilling to use the recommendation system. Therefore, the recommendation method of the existing recommendation system has the problem of relying on a large amount of user behavior data to improve the accuracy of the recommendation.

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