Article recommendation method and device and storage medium

Abstract

The invention provides an article recommendation method and device and a storage medium, and belongs to the technical field of data analysis, and the method comprises the steps: S1, importing historical long-term interest data sets of all target users and historical short-term interest data sets in one-to-one correspondence with the target users from a preset database; s2, performing data preprocessing on the historical long-term interest data set to obtain a long-term interest data training set; s3, training the training model according to the long-term interest data training set and all historical short-term interest data sets to obtain a target recommendation model; and S4, inputting the long-term interest data verification set and the short-term interest data verification set into the target recommendation model to obtain an article recommendation result. According to the method, long-term stable general hobbies and short-term dynamic preferences of the user can be mined, the influence of the interests and preferences of social friends on the user is captured, personalized recommendation with social attributes can be realized, and the recommendation accuracy and interpretability are improved.

Description

technical field [0001] The present invention mainly relates to the technical field of data analysis, and in particular to an article recommendation method, device and storage medium. Background technique [0002] As an information filtering system, the recommendation system can not only effectively solve the problem of information overload, but also has certain practical significance for promoting production and improving the quality of life. How to use deep learning technology to learn the intrinsic characteristics of users and items from various complex multi-dimensional data, and build a model that is more in line with user interests and needs, so as to improve the performance of recommendation algorithms and user satisfaction, has always been a common concern in academia and industry. direction of attention. [0003] But capturing users' interest in potential items sufficiently and accurately is a challenging task. First of all, the user's interest characteristics can ...

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Problems solved by technology

And users may also be influenced by friends in social networks, for example, users will consult friends for suggestions or follow the interaction behavior of friends for projects they are not familiar with.

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