Rating prediction method and system based on capsule network and interactive attention mechanism

An attention, capsule technology, applied in computer parts, instruments, electrical and digital data processing, etc., can solve problems such as information that cannot be further highlighted

Active Publication Date: 2021-04-23
QILU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This leads to the weight of contextual features being gradually fixed during the learning process, which cannot further highlight aspect-related information

Method used

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  • Rating prediction method and system based on capsule network and interactive attention mechanism
  • Rating prediction method and system based on capsule network and interactive attention mechanism
  • Rating prediction method and system based on capsule network and interactive attention mechanism

Examples

Experimental program
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Effect test

Embodiment 1

[0042] The purpose of this embodiment is an item recommendation method based on capsule network and interactive attention mechanism.

[0043] An item recommendation method based on capsule network and interactive attention mechanism, including:

[0044] Obtain user comment data and item comment data, and construct user documents and item documents respectively;

[0045] Inputting the user document and item document into a pre-trained rating prediction model to obtain a user-item rating prediction result;

[0046] Wherein, the rating prediction model includes a sequentially connected content coding unit, an interactive attention unit, a reverse dynamic routing unit, and a prediction unit, and the content coding unit extracts contextual features of user documents and item documents respectively; through the interactive attention The force unit learns the fine-grained correlation between the contextual features of users and items; the reverse dynamic routing unit aggregates the ...

Embodiment 2

[0112] The purpose of this embodiment is an item recommendation system based on capsule network and interactive attention mechanism.

[0113] An item recommendation system based on capsule network and interactive attention mechanism, including:

[0114] The data acquisition module is configured to acquire user comment data and item comment data, and construct user documents and item documents respectively;

[0115] The rating prediction module is configured to input the user document and item document into a pre-trained rating prediction model to obtain a user-item rating prediction result;

[0116] Wherein, the rating prediction module includes:

[0117] a content encoding unit configured to extract contextual features of the user document and the item document respectively;

[0118] An interactive attention unit configured to learn fine-grained correlations between contextual features of users and items;

[0119] The reverse dynamic routing unit is configured to aggregate...

Embodiment 3

[0122] The purpose of this embodiment is to provide an electronic device.

[0123] An electronic device, comprising a memory, a processor, and a computer program stored on the memory, when the processor executes the program, the described method for rating prediction based on a capsule network and an interactive attention mechanism is implemented, including:

[0124] Obtain user comment data and item comment data, and construct user documents and item documents respectively;

[0125] Inputting the user document and item document into a pre-trained rating prediction model to obtain a user-item rating prediction result;

[0126] Wherein, the rating prediction model includes a sequentially connected content encoding unit, an interactive attention unit, a reverse dynamic routing unit, and a prediction unit, and the content encoding unit extracts contextual features of user documents and item documents respectively; through the interactive attention The force unit learns the fine-...

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Abstract

The invention provides a rating prediction method and system based on a capsule network and an interactive attention mechanism. According to the scheme, context features of users and articles are aggregated and aspect features are generated through a designed reverse dynamic route with high interpretability; meanwhile, an interactive attention mechanism is provided, and the model learns fine-grained interactive information by constructing interaction between the user and the object context features in the plurality of feature subspaces through the interactive attention mechanism, so that the convergence phenomenon between the features in all aspects is effectively relieved, and the rating prediction accuracy is improved.

Description

technical field [0001] The disclosure belongs to the technical field of rating prediction, and in particular relates to a rating prediction method and system based on a capsule network and an interactive attention mechanism. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] As a valuable information source for recommender systems, review texts usually contain rich semantics with user preferences and item attributes. In the existing technology, the mainstream recommendation models use deep learning technology to model comment texts. These methods use an association matrix to model the correlation between users and items, and then aggregate the contextual features of users and items to form a certain Latent representations of interpretability. The inventors found that the limitation of the existing method is that the correlation matrix can ...

Claims

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Application Information

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IPC IPC(8): G06F16/33G06F16/35G06F16/9535G06K9/62
CPCG06F16/3344G06F16/353G06F16/9535G06F18/214
Inventor 杨振宇刘国敬王皓
Owner QILU UNIV OF TECH
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