Fine-grained article recommendation method and system based on comment text

Abstract

The invention provides a fine-grained article recommendation method and system based on comment texts. The method comprises the following steps: respectively obtaining a user comment text set and an article comment text set; calculating a multi-granularity incidence matrix between the user and the article by using a fine-granularity feature interaction network to obtain a 3D interaction image; inputting the 3D interaction image into a full-connection neural network and a traditional factorization machine to realize user-article score prediction, and realizing article recommendation according to a score result. According to the scheme, user and article comments are coded through a multi-level expansion convolution structure, loss of fine-grained information in the comments is avoided, feature interaction of the user and article comments is constructed under multiple granularities, and multi-granularity information is fused and processed through 3D convolution, so that related information under the multiple granularities in the comments is effectively highlighted, and the rationality and accuracy of article recommendation are effectively improved.

Description

technical field [0001] The present disclosure belongs to the technical field of item recommendation, and in particular relates to a fine-grained item recommendation method and system based on review text. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Exploiting review texts to enhance the performance of recommender systems has been proven effective in many works. Review-based recommender systems can not only alleviate the cold-start problem, but also obtain more fine-grained representations of users and items. Earlier work mainly focused on topic modeling and language modeling approaches. In recent years, researchers have used deep learning methods and achieved good results. ConvMF uses a convolutional neural network (CNN) as an automatic feature extractor to encode users (items) into low-dimensional vector representations. These ar...

AI Technical Summary

Problems solved by technology

These methods have limitations in capturing fine-grained user preferences and item attributes, because they only assign a single feature vector to user items, resulting in information at multiple granularities in reviews not being clearly captured.

Images