Recommendation algorithm based on user comments

Abstract

The invention provides a recommendation algorithm based on user comments. The method is based on a comment recommendation system of deep learning. according to past browsing purchase and comment records of a user, score records of commodities and comments of customers who have purchased the commodities, the preference degree of the user for the commodities is speculated, finally, the recommendation system ranks scores, and the first N commodities with high scores are recommended to the user. According to the method, the comment text information is vectorized by utilizing a natural language processing technology; the importance of each comment and the influence of each comment on the potential customer are calculated according to an attention mechanism algorithm; a convolutional neural network is adopted to capture an interaction feature matrix of a user commodity, so that an interaction vector of the user commodity is generated, finally, the generated interaction vector is input into an FM prediction machine to predict a score, the higher the prediction score is, the more likely the user likes the commodity, and finally, sorting is performed according to the score to recommend N commodities before scoring to the user.

Description

technical field [0001] The present invention relates to the technical field of recommendation algorithms, and more specifically, to a recommendation algorithm based on user comments. Background technique [0002] Recommendation services are ubiquitous in people's daily life, and they can help users discover potentially interesting products or useful services. The recommendation system first collects the user's historical behavior data, then generates the user product rating matrix, and then uses the relevant recommendation technology in the field of machine learning to form a personalized recommendation for the user. A good recommendation system can not only help users find what they like, but also increase the sales and transaction conversion rate of shopping websites, and increase customer loyalty to the website. In the past few years, traditional recommender systems have focused on user-item interactions, such as ratings, purchases, or click records. For example, the ma...

AI Technical Summary

Problems solved by technology

For example, when a user is a new user of shopping or a product has just been put on the shopping website, the traditional recommendation system cannot recommend suitable products to the user because of the lack of purchase records of the new user.

For example, the number of products on a shopping website is far greater than that of users, and the traditional user product matrix becomes a sparse matrix, so the recommendation effect will be much worse

Images