Side information fusion for sequential recommendation (SR) aims to
effectively leverage various side information to enhance the performance of
next-item prediction. Most state-of-the-art methods build on self-attention
networks and focus on exploring various solutions to integrate the item
embedding and side information embeddings before the attention layer. However,
our analysis shows that the early integration of various types of embeddings
limits the expressiveness of attention matrices due to a rank bottleneck and
constrains the flexibility of gradients. Also, it involves mixed correlations
among the different heterogeneous information resources, which brings extra
disturbance to attention calculation. Motivated by this, we propose Decoupled
Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the
side information from the input to the attention layer and decouples the
attention calculation of various side information and item representation. We
theoretically and empirically show that the proposed solution allows
higher-rank attention matrices and flexible gradients to enhance the modeling
capacity of side information fusion. Also, auxiliary attribute predictors are
proposed to further activate the beneficial interaction between side
information and item representation learning. Extensive experiments on four
real-world datasets demonstrate that our proposed solution stably outperforms
state-of-the-art SR models. Further studies show that our proposed solution can
be readily incorporated into current attention-based SR models and
significantly boost performance. Our source code is available at
this https URL.

通过提出一个新的解决方案，Decoupled Side Information Fusion, 提高了序列推荐中不同侧面信息的建模能力，通过实验验证了该方法在四个真实数据集上的有效性。

基于解耦的顺序推荐侧信息融合

Decoupled Side Information Fusion for Sequential Recommendation

Generative Adversarial Networks (GANs) have brought about rapid progress
towards generating photorealistic images. Yet the equitable allocation of their
modeling capacity among subgroups has received less attention, which could lead
to potential biases against underrepresented minorities if left uncontrolled.
In this work, we first formalize the problem of minority inclusion as one of
data coverage, and then propose to improve data coverage by harmonizing
adversarial training with reconstructive generation. The experiments show that
our method outperforms the existing state-of-the-art methods in terms of data
coverage on both seen and unseen data. We develop an extension that allows
explicit control over the minority subgroups that the model should ensure to
include, and validate its effectiveness at little compromise from the overall
performance on the entire dataset. Code, models, and supplemental videos are
available at GitHub.

通过协调对抗训练和重构生成来提高数据覆盖率，并扩展了一个允许显式控制模型应该包含的少数群体的方法。