Personalized recommendation system based on user memory network and deep model with tree structure

Abstract

The invention discloses a personalized recommendation system based on a user memory network and a deep model with a tree structure. The personalized recommendation system is characterized by comprising a user memory module, a commodity body module and a prediction module. Wherein the user memory module is used for capturing historical data of a user; the user memory module is composed of a context-based long and short memory network framework, and captures interest dynamics of a user through short-term memory and long-term memory. The short-term memory is used for capturing records of the userfor purchasing commodities recently, and obtaining short-term memory mapping of the user through the records; the long-term memory summarizes and records the characteristics of the commodity which the user is interested in according to the long-term purchasing habit of the user and a large number of purchasing records of the user, and long-term memory mapping of the user is obtained through the records. The commodity body module obtains mapping information of the commodities through the associated information between the commodities and historical purchase records of the user; and the prediction module performs final recommendation prediction in combination with the short-term memory mapping, the long-term memory mapping and the commodity mapping output by the user memory module and the commodity body module.

Description

technical field [0001] The invention relates to the field of electronic commerce, in particular to a personalized recommendation system that mines user historical purchase records and predicts customers' shopping intentions. Background technique [0002] 90% of the data we face today was generated in the past two years. Faced with the massive data growth, for Internet users, it undoubtedly means that more information can be obtained, and people gradually start from information. The age of scarcity has entered the age of information overload. In this era, no matter as a producer or a consumer of information, we will face the huge challenges brought by massive data. First of all, as far as information consumers are concerned, that is, Internet users, they will find that it will become more and more difficult to find the information they are interested in among a large amount of information. For information producers, how to attract the attention of the majority of users and ...

AI Technical Summary

Problems solved by technology

Although the existing solutions have achieved certain results, there are still areas for urgent improvement

Specifically, it includes the following problems: 1. Most of the existing methods tend to compress the user's history into a fixed hidden expression in the process of sequential recommendation. In the process, it will weaken the correlation between those highly related products, or ignore some signals, making it difficult for us to understand or explain the sequential recommendation process

2. Since the collaborative filtering method only considers similar users or products, it will lead to a lack of comprehension and diversity in the recommendation results

3. Although the model-based method is considered to provide more accurate recommendation results, when the number of users and products is large enough, it becomes an important challenge to train these data well and stably

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