Personalized recommendation system based on deep learning under social network

Abstract

The invention discloses a personalized recommendation system based on deep learning under a social network. The system mainly comprises an offline learning module and an online recommendation module. The offline learning module firstly generates a training sample seat to construct a deep convolutional neural network learning module with an attention mechanism and carries out iterative optimization on parameters in the learning module; and the online recommendation module carries out real-time item recommendation on a newly-registered user based on the learning model obtained through training. Compared with the prior art, the system has the advantages of high accuracy, fast speed and simplicity and easiness in implementation and can be effectively applied to the fields, such as electronic commerce, public opinion monitoring, intelligent transportation and medical treatment and health.

Description

technical field [0001] The invention relates to the technical field of information recommendation, in particular to a deep learning-based personalized recommendation system under social networks. Background technique [0002] The informal concept of the recommendation system is the definition given by Resnick and Varian in 1997: "It uses e-commerce websites to provide customers with product information and suggestions, and helps users decide what products to buy, and simulates salespeople to help guide The process by which a user completes an online purchase". [0003] The recommendation system can dig out the user's potential favorite content, reduce the interference of useless information on the user, and enable the user to quickly find the products they want to buy, the news they are interested in, and potential friends on the Internet. And these recommendation results are dynamic, because the user's interest changes with time and scene changes, the final recommendation ...

AI Technical Summary

Problems solved by technology

At the same time, when a new user logs in to an e-commerce service platform based on a social network, the recommendation system cannot give good recommendation results due to the lack of background or interest data related to the new user. This is the recommendation system in a social network environment. cold start problem

The cold start problem leads to poor recommendation quality of the recommender system, which cannot meet the needs of newly registered users, and they will most likely not want to log in to the e-commerce service platform again

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