A sequence recommendation method and apparatus based on adaptive attention

A recommendation method and self-adaptive technology, applied in the field of recommendation, can solve the problems of insufficient capture of user diversity sequence decision-making dynamic process, poor performance, etc.

Active Publication Date: 2019-02-19
SUZHOU UNIV
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Problems solved by technology

Therefore, existing methods are insufficient to capture the dynamic process of user diversity sequential decision-making, resulting in poor performance

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  • A sequence recommendation method and apparatus based on adaptive attention
  • A sequence recommendation method and apparatus based on adaptive attention
  • A sequence recommendation method and apparatus based on adaptive attention

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[0048]In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0049] The core of the present invention is to provide a sequence recommendation method based on adaptive attention, which utilizes the pre-trained adaptive attention-aware GRU network for sequence recommendation. The adaptive attention-aware GRU network is based on recurrent neural network and a new adaptive attention mechanism to learn the sequential representation of adaptive users. Specifically, an attenti...

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Abstract

The invention discloses a sequence recommendation method based on adaptive attention. The method comprises the following steps: determining input adaptive attention at an input layer of an adaptive attention perception GRU network obtained by pre-training; applying the input adaptive attention to the history interactive item sequence to obtain the input sequence; Based on the combination of historical interactive item sequence and input sequence, the output result of input layer is obtained. Hidden adaptive attention is determined in that hidden state layer, and the hidden adaptive attention is acted on the output result of the input layer to obtain a hidden state sequence; the output result of the hidden state layer is obtained; At the output layer of the adaptive attention-aware GRU network, the recommended items are determined according to the output results of the hidden state layer. According to the technical proposal provided by the embodiment of the invention, the recommendationperformance is improved. The invention also discloses a sequence recommendation device based on adaptive attention, which has corresponding technical effects.

Description

technical field [0001] The present invention relates to the technical field of recommendation, in particular to a sequence recommendation method and device based on adaptive attention. Background technique [0002] With the explosive growth of network information, recommender systems play an increasingly important role in many online services. Commonly used recommendation methods in recommender systems include general recommendation and sequence recommendation. General recommendation refers to recommending items by modeling the user's overall preferences through the user's historical interaction items. The key idea is collaborative filtering (CF), which can be further divided into memory-based CF and model-based CF. General recommendations can capture users' overall preferences, but without sequential behavior modeling, it is difficult to make recommendations directly based on users' recent interaction items. Sequential recommendation considers a user's interacting items ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06N3/04
CPCG06N3/045Y02D30/70
Inventor 赵朋朋罗安靖周晓方崔志明
Owner SUZHOU UNIV
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