TL;DR
This paper introduces Triplet Importance Learning (TIL), a novel framework that adaptively assigns importance scores to training triplets in implicit feedback-based recommendation, improving model performance without auxiliary data.
Contribution
The paper proposes TIL, a bilevel optimization framework that adaptively learns triplet importance scores, enhancing recommendation accuracy across different models without relying on auxiliary information.
Findings
TIL improves recommendation performance by 3-21% in Recall@k.
The framework is compatible with various models like MF and GNN.
TIL outperforms state-of-the-art methods on real-world datasets.
Abstract
Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user. However, most of these methods treat all the training triplets equally, which ignores the subtle difference between different positive or negative items. On the other hand, even though some other works make use of the auxiliary information (e.g., dwell time) of user behaviors to capture this subtle difference, such auxiliary information is hard to obtain. To mitigate the aforementioned problems, we propose a novel training framework named Triplet Importance Learning (TIL), which adaptively…
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Taxonomy
MethodsGraph Neural Network
