Recommendation method and device based on attention mechanism, equipment and storage medium

A recommendation method and attention technology, applied in the field of data processing, can solve problems such as difficult to capture

Active Publication Date: 2019-08-06
SUZHOU UNIV
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Problems solved by technology

However, user preferences within and between carts are diverse, which makes it difficult to capture based on fixed attention mechanisms

Method used

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  • Recommendation method and device based on attention mechanism, equipment and storage medium
  • Recommendation method and device based on attention mechanism, equipment and storage medium
  • Recommendation method and device based on attention mechanism, equipment and storage medium

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Embodiment Construction

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0060] It should be noted that the recurrent neural network (RNN) is widely used in sequence recommendation to describe the characteristics of user interests over time. For example, some scholars use RNN to mine dynamic user and item preferences in trajectory data. Since the monotonic assumption of RNN limits the modeling of short-term interests of users, some scholars have proposed an RNN solution based on attention. This automatically assigns different impact we...

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Abstract

The invention discloses a recommendation method, device and equipment based on a self-adaptive attention mechanism and a computer readable storage medium. The method includes obtaining a short-term preference vector of the current shopping cart and a long-term preference vector among all historical shopping cart vectors based on a self-adaptive attention mechanism;reflecting the short-term preference diversity and the long-term preference diversity of the user respectively according to the short-term preference vector and the long-term preference vector, and finally, inputting the combinationof the short-term preference diversity and the long-term preference diversity into the recurrent neural network LSTM to better learn the sequence behavior of the user, so that interested commodities are accurately recommended to the user.

Description

technical field [0001] The present invention relates to the technical field of data processing, and more specifically, to a recommendation method, device, equipment and computer-readable storage medium based on an adaptive attention mechanism. Background technique [0002] With the rapid development of e-commerce, many companies have established shopping platforms to facilitate shopping for users. One of the most important tasks in e-commerce is to be able to discover the user's purchasing habits and their evolution in time, and to recommend the next product to the user accurately. This can not only increase traffic and profits for service providers, but also help customers find interesting products more easily. Therefore, the user's shopping history is represented as a sequence of shopping carts, and the next shopping cart recommendation is formalized as a sequence prediction problem. That is, given a user's shopping cart records, it is possible to predict what the user w...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06N3/04G06N3/08
CPCG06Q30/0631G06N3/049G06N3/08
Inventor 赵朋朋车彬彬周晓方崔志明
Owner SUZHOU UNIV
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