Information flow recommendation method and device based on deep reinforcement learning, equipment and medium

Abstract

The embodiment of the invention discloses an information flow recommendation method and a device based on deep reinforcement learning, equipment and a medium, and relates to the technical field of information processing. The method comprises the steps of collecting a historical click sequence of a target user; calling a preset actor neural network and a preset critic neural network; generating a user recommendation list, and displaying the user recommendation list to the target user to obtain feedback result data and a new historical click sequence generated after feedback; calculating a timedifference error; updating parameters in the critic neural network and the actor neural network; and generating a new user recommendation list, and displaying the new user recommendation list until feedback result data of the target user for the new user recommendation list and a new historical click sequence generated after feedback cannot be obtained. According to the method, the interactivity between the recommendation system and the user is enhanced, the feedback of the user is utilized in real time, the recommendation engine can be continuously optimized, the recommendation quality is improved, the user experience is improved, and the user is effectively attracted to remain.

Description

technical field [0001] The embodiments of the present application relate to the field of information processing technology, in particular, a method, device, device and medium for recommending information flow based on deep reinforcement learning. Background technique [0002] With the development of artificial intelligence, more and more product applications use artificial intelligence to improve the interactive experience between users and products, such as recommending products of interest to users based on their interest characteristics and purchasing behavior. With the continuous expansion of the scale of e-commerce and the rapid growth of the number and types of commodities, customers need to spend a lot of time to find the commodities they want to buy. This process of browsing a large amount of irrelevant information and products will lead to the continuous loss of consumers who are drowning in the problem of information overload. In order to solve these problems, the...

AI Technical Summary

Problems solved by technology

The existing recommendation system lacks interactivity with users, which may easily lead to users not being interested in the recommended information, and the user's access experience is not improved in the information flow scenario.

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