Product recommendation method and system based on deep learning

Abstract

The invention provides a product recommendation method based on deep learning. The method comprises the following steps: S1, crawling comment data of a product by means of a crawler; S2, conducting data preprocessing of the comment data; S3, conducting feature extraction of the data; S4, carrying out fine grain analysis of product comments; S5, giving quantitative marks to the product comments; and S6, with collaborative filtering combined, performing product recommendation. Meanwhile, the invention also provides a product recommendation system based on deep learning. Compared with the prior art, the method and the system combine the deep learning method to refine texts and quantifies the texts through a fuzzy membership function, can convert user comments into the marks of attributes of a product, then integrates a collaborative filtering method for recommendation, and can achieve better recommendation results.

Description

technical field [0001] The present invention relates to a recommendation method, in particular to a product recommendation method and system based on deep learning. Background technique [0002] With the development of the Internet, e-commerce has emerged as a new industry. E-commerce moves goods from physical stores to the virtual environment of the network, allowing users to shop without leaving home. The advantage of virtual shopping is that it reduces the seller's cost and improves the buyer's shopping experience. But for online shopping users, the variety of products on the Internet brings troubles to selection. The way to solve this problem is to use the recommendation system to make personalized item recommendations for users, that is to say, the user's potential interest can be obtained by analyzing the user's past shopping behaviors such as browsing, clicking, purchasing, and commenting, as well as the user's own attributes. , so as to recommend the products that ...

AI Technical Summary

Problems solved by technology

[0004] However, most recommendation systems rely on the user's historical rating records for items to predict new product ratings. However, for some products, such as hotels and restaurants, users only have review records for products. good way to deal with

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