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
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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
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