Recommendation method and system based on recurrent neural network

Abstract

The present invention provides a recommendation method and system based on a recurrent neural network. The method comprises: using a recurrent neural network model to model a user behavior and using the model for a recommendation system; constructing a recurrent neural network model learning method for the recommendation system by combining a BP algorithm principle with a neural network structure of the present invention; training the recurrent neural network according to a feature of the recommendation system, and establishing a unique neural network structure, so as to generate a recommendation list for different users corresponding to interests thereof according to the new neural network structure. According to the recommendation method and system, user preferences are learned from history behaviors of the user by using the recurrent neural network, and a recommendation service is provided for the user on this basis; user behaviors of different types can be represented uniformly based on a time sequence according to a unique recursive structure of the recurrent neural network; and a deep neural network structure is formed if the recurrent neural network expands in time, and the user behaviors with relatively large randomness can be represented more accurately.

Description

technical field [0001] The invention relates to the technical field of system recommendation, in particular to a recommendation method and system based on a recursive neural network. Background technique [0002] Currently, network service providers provide users with online services such as news, commodities, pictures, videos, audios, documents, etc. (hereinafter collectively referred to as items). In order to provide better services for users, service providers will record the historical behavior of users, such as recording which items the user has purchased (used), evaluation of items, clicks, forwarding, comments, etc. According to the user's historical behavior, providing users with accurate recommendations is a major challenge for the recommendation system, which is mainly manifested in: [0003] First, although user historical behavior provides important information for understanding user preferences and recommending items for users. But various user behaviors have ...

AI Technical Summary

Problems solved by technology

[0004] Second, user behavior exhibits enormous uncertainty in terms of expressed user preferences

For example, behaviors such as clicking and forwarding cannot clearly indicate whether the user likes the item

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