.png){kind=icon}
Return
SPECN:sequential patterns enhanced capsule network for sequential recommendation
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-024-06159-6.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sequential patterns and the order of items in sequences are particularly important for sequential recommendation (SR), which decide what item will interact with the user. However, there are still some problems for the existing methods: (1) They treat all the features of each item equally, we believe that those features the user pays more attention to of each item play a key role to predict next item. (2) Many methods not only ignore sequential patterns and the order of sequences, but also cannot highlight more important features, they only focus on whether the features exist. To address these issues, we propose the Sequential Patterns Enhanced Capsule Network (SPECN). SPECN leverages a self-attention mechanism, using user information as a guide to highlight the most relevant features for each item, then concatenates these features with the original item features in the sequence.SPECN applies horizontal and vertical capsule networks which package neurons into vectors to extract sequential patterns features and the order of sequences. The horizontal capsule network enhances sequential pattern features by learning both the original and user-focused features of individual or adjacent items, containing original features and those features that the user pays more attention to of single item or adjacent items' features (features of the previous item that the users pay more attention to and features of the current item) to enhance the sequential patterns features. The vertical capsule network captures finer-grained feature representations for each item, improving the recommendation quality. We conduct several experiments on three real-world datasets to demonstrate the superiority of SPECN, outperforming existing methods in terms of accuracy and robustness.
Keywords:
Sequential recommendation
Capsule network
Self-attention
Convolutional neural network
Journal
IF:
3.5
Papers:
7.7K
Citations:
1.7W
Organization
Cited Papers
Nonsingular Terminal Sliding Mode Control Based on Adaptive Barrier Function for nth-Order Perturbed Nonlinear Systems.png)
Mathematics
OAAI
MathematicsIF0
OAAIShareSave
ShareSaveAn evolutionary analysis of new energy and industry policy tools in China based on large-scale policy topic modelingPLOS ONEOAAI
PLOS ONEIF0
OAAIShareSaveShareSaveShareSave
View more