A Sequence Recommendation Method Based on Differential Modeling of User Behavior

Abstract

The invention discloses a sequence recommendation method based on user behavior difference modeling, which includes: obtaining user's historical behavior information; calculating commodity feature vectors according to the obtained historical behavior information; model, through two different neural network architectures to obtain the user's current needs and historical preferences; according to the user's current purchase needs and historical preferences, through joint learning to predict the next product that the user is interested in, and in the product vector space Matching, find multiple products that are closest to the predicted results in the product vector space, and generate product recommendation sequences. This method intelligently understands the current needs and long-term preferences of users in purchasing decisions by modeling the differences in user time-series behaviors, and can provide users with accurate sequential recommendation services.

Description

technical field [0001] The invention relates to the technical fields of machine learning and e-commerce, in particular to a sequence recommendation method based on user behavior distinction modeling. Background technique [0002] With the continuous development of online shopping platforms, recommender systems have become an irreplaceable and important part of e-commerce. The recommendation system can learn the preference information hidden in the user's historical behavior, so as to further predict the user's shopping behavior, help customers choose satisfactory products, and promote the revenue of e-commerce platforms. Therefore, how to efficiently and accurately provide users with personalized product recommendation services has always been an important research issue in academia and industry. [0003] At present, there are two main categories of research on recommender systems: [0004] 1) Recommendation system based on user static preference [0005] Content-based, c...

AI Technical Summary

Problems solved by technology

This short-session sequence recommendation method can model the dynamic changes of user behavior in the short term through deep neural networks, but this method ignores the user's own preferences, which makes the recommendation results often meet the user's needs but not the user's favorite type.

At the same time, neither method can deeply model the dynamic changes in the entire decision-making process of users, and does not specifically analyze the different degrees of preference expressed by different behaviors of users.

Therefore, it is difficult to accurately model the complete decision-making process when users choose goods or services by using existing recommendation methods, and the user needs and preferences cannot be combined, resulting in recommended content that fails to meet user expectations

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