Dongjun Lee

In real-world recommender model deployments, the models are typically retrained and deployed repeatedly. It is the rule-of-thumb to periodically retrain recommender models to capture up-to-date user behavior and item trends. However, the harm caused by delayed model updates has not been investigated extensively yet. in this perspective paper, we formulate the delayed model update problem and quantitatively demonstrate the delayed model update actually harms the model performance by increasing the number of cold users and cold items increase and decreasing overall model performances. These effects vary across different domains having different characteristics. Upon these findings, we further argue that although the delayed model update has negative effects on online recommender model deployment, yet it has not gathered enough attention from research communities. We argue our verification of the relationship between the model update cycle and model performance calls for further research such as faster model training, and more efficient data pipelines to keep the model more up-to-date with the latest user behaviors and item trends.

Deep learning models exhibit a dependency on peripheral attributes of input data, such as shapes and colors, leading the models to become biased towards these certain attributes that result in subsequent degradation of performance. In this paper, we alleviate this problem by presenting sysname, a novel framework that tackles dataset bias by leveraging generative models to amplify bias and facilitate the learning of debiased representations of the classifier. Our method involves three major steps. We initially train a biased classifier, denoted as f_b, on a biased dataset and extract the top-K biased-conflict samples. Next, we train a generator solely on a bias-conflict dataset comprised of these top-K samples, aiming to learn the distribution of bias-conflict samples. Finally, we re-train the classifier on the newly constructed debiased dataset, which combines the original and amplified data. This allows the biased classifier to competently learn debiased representation. Extensive experiments validate that our proposed method effectively debiases the biased classifier.

Sequential recommendation aims to predict users’ next actions by analyzing their historical behavior. Lately, contrastive learning has become prominent in this domain, especially when user interactions with items are sparse. Although data augmentation methods have flourished in fields like computer vision, their potential in sequential recommendation remains under-explored. Thus, we present Hierarchical Contrastive Learning with Multiple Augmentations for Sequential Recommendation (HCLRec), a novel framework that harnesses multiple augmentation techniques to create diverse views on user sequences. This framework systematically employs existing augmentation techniques, creating a hierarchy to generate varied views. First, we augment the input sequences to various views using multiple augmentations. Through the continuous composition of these augmentation methods, we formulate both low-level and high-level view pairs. Second, an effective sequence-based encoder is used to embed input sequences, complemented by the supplementary blocks to capture users’ nonlinear behaviors, which are further varied by augmentations. Input sequences are routed to subsequent layers based on the number of augmentations applied, helping the model discern intricate sequential patterns intensified by these augmentations. Finally, contrastive losses is calculated between view pairs of the same level within each layer. This allows the encoder to learn from the contrastive losses between augmented views of the same level, and the gap caused by different information between the low-level views and high-level views by multiple augmentations is reduced. In evaluations, HCLRec outperforms state-of-the-art methods by up to 7.22% and demonstrates its effectiveness in handling sparse data.