EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention

Conceitos Básicos

Proposing EulerFormer, a transformer variant with complex vector attention, to enhance user behavior modeling by integrating semantic and positional differences effectively.

Resumo

The paper introduces EulerFormer, a transformer variant with complex vector attention, to address limitations in modeling semantic and positional differences.
EulerFormer unifies semantic and positional differences in a complex vector space, improving sequence modeling.
The model involves Euler transformation for efficient token embedding transformation and adaptive rotation for integrating semantic and positional information.
A phase contrastive learning task is proposed to enhance isotropy in contextual representations.
Experimental results show EulerFormer outperforms existing methods on four public datasets, demonstrating its effectiveness and efficiency.

Estatísticas

Due to the permutation-equivariant nature, positional encoding is used to enhance attention between token representations.
EulerFormer involves two key technical improvements: transformation function for transforming sequence tokens into complex vectors and differential rotation mechanism for adaptive integration of semantic and positional information.
The proposed phase contrastive learning task aims to improve the isotropy of contextual representations.

Citações

"Our theoretical framework possesses a high degree of completeness and generality."
"EulerFormer can serve as a powerful substitute for transformer backbone in user behavior modeling."

Principais Insights Extraídos De

EulerFormer

by Zhen Tian,Wa... às arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17729.pdf

Perguntas Mais Profundas

How can EulerFormer's adaptive rotation mechanism improve the modeling of semantic and positional differences

EulerFormer's adaptive rotation mechanism improves the modeling of semantic and positional differences by allowing for the adjustment of semantic differences according to varying contexts. By introducing learnable parameters to adapt the semantic differences, EulerFormer can effectively align the semantic and positional information. This adaptive integration ensures that the model can capture the varying semantic contexts in different interaction scenarios, leading to a more accurate representation of user preferences. The mechanism enables the model to flexibly adjust the semantic angles and integrate them with positional differences, enhancing the overall capacity of the model in user behavior modeling.

What are the implications of EulerFormer's phase contrastive learning task for enhancing isotropy in contextual representations

EulerFormer's phase contrastive learning task plays a crucial role in enhancing isotropy in contextual representations. By focusing on adjusting the orientation of token embeddings while keeping their magnitude constant, the phase contrastive task aims to improve the discriminability of different items in the sequence. This approach helps in enhancing the orientation relationships of vectors, leading to more isotropic representations. By decoupling the semantic and positional differences and focusing on adjusting the orientation of embeddings, EulerFormer's phase contrastive learning task contributes to creating more balanced and uniform contextual representations, ultimately improving the model's performance in sequential recommendation tasks.

How might EulerFormer's theoretical framework impact the future development of user behavior modeling techniques

EulerFormer's theoretical framework has significant implications for the future development of user behavior modeling techniques. By providing a unified mathematical form to model both semantic and positional differences in complex vector space, EulerFormer offers a more expressive capacity in sequence modeling. This framework allows for the adaptive integration of semantic and positional information, enhancing the model's robustness to semantic variations and improving its theoretical properties, such as long-term decay. The flexibility and generality of EulerFormer's theoretical framework make it a powerful tool for modeling complex sequential patterns in user behavior data. This framework could potentially inspire the development of more advanced and effective user behavior modeling techniques in the future, with a focus on integrating semantic and positional information in a unified manner.