Recommendation system and method based on user and project coupling relationship analysis

Abstract

The invention discloses a recommendation system and method based on user and project coupling relationship analysis. The method comprises the steps of data acquisition and processing, data set division, coupling model and training model construction and project recommendation. According to the method, the very microscopic coupling relationship between the user characteristics and the project characteristics is considered, and when the scoring information is sparse, the coupling relationship can recommend favorite projects to the user, so that the recommendation quality is improved; and an Attention mechanism is adopted to capture preference degrees of the user for different features of the project, so that the recommendation system has a better recommendation effect and interpretability. Moreover, the explicit features of the user and the project are extracted from the comment text by utilizing Doc2vec; the dimension of the user / project explicit features is reduced, the model operationspeed is increased, the recommendation accuracy is improved, and compared with matrix decomposition, the nonlinearity of the convolutional neural network and the deep neural network adopted by the method is beneficial to interaction between learning features at a deeper level.

Description

technical field

[0001] The invention belongs to the technical field of natural language processing and computer artificial intelligence, and in particular relates to a recommendation system and method based on the analysis of coupling relationship between users and items. Background technique

[0002] With the rapid development and popularization of Internet information technology, people are more and more fond of shopping online and commenting on and scoring items online. However, facing so many similar items on the e-commerce platform, consumers have to spend a lot of time choosing on your favorite projects. Therefore, it is very important to recommend items that users like and have interpretability. Many existing recommendation systems have poor explainability and consider users and items to be independent and identically distributed, ignoring the heterogeneity and coupling between users and items. In fact, there are various coupling relationships between users and items...

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