Reusable Self-attention-Based Recommender System for Fashion

Celikik, Marjan; Wasilewski, Jacek; Sahar, Mbarek,; Celayes, Pablo Gabriel; Gagliardi, Pierre; Pham, Duy; Karessli, Nour; Ramallo, Ana Peleteiro

doi:10.1007/978-3-031-22192-7_3

Cited by 1 publication

(1 citation statement)

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“…The attention mechanism can be utilized as a resource allocation schema to concentrate on distinctive parts when dealing with overloaded information [7]. Most of the attention mechanisms are focused attention which has been applied to various fields, such as image-based analysis [8,9], text classification [10,11], video classification [12], image captioning [13], and recommendation [14,15,16].…”

Section: Attention Mechanismmentioning

confidence: 99%

Applying self-attention model to learn both Empirical Risk Minimization and Invariant Risk Minimization for multimedia recommendation

Zhao,

Huang,

Zhao

et al. 2024

ACE

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Multimedia recommendation systems have many applications in our daily life. However, how accurately capture a customer's preference is an issue that is difficult to deal with. The proposed Invariant Risk Minimization (IRM) and Empirical Risk Minimization (ERM) are ways to learn a customer's preference. Still, both frameworks show some limitations: although ERM performs excellently in a single environment, it fails to generalize well when faced with multiple and new domains. On the other hand, IRM learns invariant features across heterogeneous environments, but it lacks theoretical guarantees and performs less effectively where the invariants are unclear. This paper proposes an ERM and IRM Optimized Rating Framework (EIOR) as our final recommender model with direct rating scores. The EIOR enhances the accuracy and functionality of the multimedia recommendation systems by utilizing self-attention mechanisms to combine IRM and ERM with adjusted attention weights. Specifically, IRM learns invariant parts across different environments, while ERM learns variant parts. With self-attention, we can adaptively allocate attention weights for the two pieces and seek the optimal pair of attention weights based on the loss function. We demonstrate EIOR on a cutting-edge recommender model UltraGCN and use the open multimedia dataset of TikTok to finish all the experiments. The results validate the effectiveness of EIOR by comparing purely operating invariant representations alone with the framework of IRM.

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