Time sequence perception dynamic social scene recommendation method

Abstract

The invention discloses a time sequence perception dynamic social scene recommendation method. The method comprises the following steps: obtaining basic data for analysis from historical consumption behaviors and social behavior records of a user; modeling a user time sequence consumption behavior and a time sequence social behavior according to the basic data so as to restore the decision processof the user in the historical consumption behavior by utilizing the obtained dynamic user personal preference and dynamic social background information, estimating the relative sorting of the commodities in combination with the decision function of the user, and realizing the training of related parameters in the decision function of the user; for a new commodity, calculating a preference score of the user for each commodity based on the decision function completing parameter training, and then predicting a result selected by the user in a stable matching mode and recommending the result. According to the method, the accurate portrait of the user can be realized, the performance of decision analysis of the user and even commodity recommendation can be improved, and the effect of multiplepurposes is realized.

Description

technical field [0001] The present invention relates to the field of deep learning and recommendation systems, in particular to a timing-aware dynamic social scene recommendation method. Background technique [0002] The recommendation system is an information filtering system, which aims to analyze user preferences and screen information through user behavior data on e-commerce platforms, so as to provide users with personalized recommendation services. At present, recommendation systems have been widely used in various industries, and the objects that can be recommended include a variety of rich goods and services such as movies, books, music, and news. In recent years, with the development of social platforms and the combination of social elements and emerging business applications, the social behavior between users has become an important basis for recommending products, which means that users' choices on the platform are affected by their social relationships. Therefor...

AI Technical Summary

Problems solved by technology

[0003] In the current research work and patents, no research has been found that combines dynamic social influence and user sequence behavior to recommend users, especially research that incorporates dynamic social influence into the category of time series for representation modeling

Images