Text information-based deep reinforcement learning interactive recommendation method and system

Abstract

The invention discloses a text information-based deep reinforcement learning interactive recommendation method and system, and belongs to the field of interactive personalized recommendation, and themethod comprises the steps: respectively converting commodities and users into commodity vectors and user vectors based on text information, and carrying out the clustering of the users; establishinga recommendation model for each user category based on DDPG, and establishing a global environment simulator; for any recommendation model, constructing an action candidate set Can (ui, t) in the t-thround of interaction; enabling the strategy network to take the state st of the current user as an input, obtaining a strategy vector pt, and selecting an action vector at from the Can (ui, t) according to the pt; enabling the valuation network to take the pt and st as input, and calculating a Q value used for evaluating the advantages and disadvantages of pt; in each round of interaction, enabling the environment simulator to calculate a feedback reward value and update the state of the current user; and outputting a feedback reward value to the value estimation network, correcting the valueestimation network, reversely conducting the Q value to the strategy network, and adjusting the strategy network to obtain a better strategy vector. The recommendation efficiency and the recommendation accuracy can be improved.

Description

technical field [0001] The present invention belongs to the field of interactive personalized recommendation, and more specifically, relates to a deep reinforcement learning interactive recommendation method and system based on text information. Background technique [0002] With the rapid growth of the amount of information on the Internet, the differences between information are also increasing, and at the same time, the different choices of different information by users also show obvious clustering characteristics. In order to continuously make personalized recommendations, a series of research results on Interactive Recommender System (IRS) have emerged. Reinforcement learning, which can continuously learn and maximize rewards during dynamic interactions, has recently attracted a lot of attention in IRS. [0003] Reinforcement learning is an important branch of machine learning, and it is a kind of method to find the optimal strategy in the interaction with the environ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the defects and improvement needs of the prior art, the present invention provides a deep reinforcement learning interactive recommendation method and system based on text information, the purpose of which is to solve the problems of low efficiency and recommendation The problem of poor performance

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