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Comparative Convolutional Dynamic Multi-Attention Recommendation Model | IEEE Journals & Magazine | IEEE Xplore

Comparative Convolutional Dynamic Multi-Attention Recommendation Model


Abstract:

Recently, an attention mechanism has been used to help recommender systems grasp user interests more accurately. It focuses on their pivotal interests from a psychology p...Show More

Abstract:

Recently, an attention mechanism has been used to help recommender systems grasp user interests more accurately. It focuses on their pivotal interests from a psychology perspective. However, most current studies based on it only focus on part of user interests; they have not mined user preferences thoroughly. To address the above problem, we propose a novel recommendation model: comparative convolutional dynamic multi-attention (CCDMA). This model provides a more accurate approach to represent user and item features and uses multi-attention-based convolutional neural networks to extract user and item latent feature vectors dynamically. The multi-attention mechanism considers both self-attention and cross-attention. Self-attention refers to the internal attention within users and items; cross-attention is the mutual attention between users and items. Moreover, we propose an optimized comparative learning framework that can mine the ternary relationships between one user and a pair of items, focusing on their relative relationship and the internal link between a pair of items. Extensive experiments on several real-world data sets show that the CCDMA model significantly outperforms state-of-the-art baselines in terms of different evaluation metrics.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 33, Issue: 8, August 2022)
Page(s): 3510 - 3521
Date of Publication: 08 February 2021

ISSN Information:

PubMed ID: 33556019

Funding Agency:


References

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