Extreme multi-label classification (XMC) aims at finding multiple relevant labels for a given sample from a huge label set at the industrial scale. The XMC problem inherently poses two challenges: scalability and label sparsity - the number of labels is too large, and labels follow the long-tail distribution. To resolve these problems, we propose a novel Mixup-based augmentation method for long-tail labels, called TailMix. Building upon the partition-based model, TailMix utilizes the context vectors generated from the label attention layer. It first selectively chooses two context vectors using the inverse propensity score of labels and the label proximity graph representing the co-occurrence of labels. Using two context vectors, it augments new samples with the long-tail label to improve the accuracy of long-tail labels. Despite its simplicity, experimental results show that TailMix consistently outperforms other augmentation methods on three benchmark datasets, especially for long-tail labels in terms of two metrics, [email protected] and [email protected]

Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-session characteristics but pay less attention to inter-session relationships of items, which has the potential to improve accuracy. Another critical aspect of recommender systems is computational efficiency and scalability, considering practical feasibility in commercial applications. To account for both accuracy and scalability, we propose a novel session-based recommendation with a random walk, namely S-Walk. Precisely, S-Walk effectively captures intra- and inter-session correlations by handling high-order relationships among items using random walks with restart (RWR). By adopting linear models with closed-form solutions for transition and teleportation matrices that constitute RWR, S-Walk is highly efficient and scalable. Extensive experiments demonstrate that S-Walk achieves comparable or state-of-the-art performance in various metrics on four benchmark datasets. Moreover, the model learned by S-Walk can be highly compressed without sacrificing accuracy, conducting two or more orders of magnitude faster inference than existing DNN-based models, making it suitable for large-scale commercial systems.

Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Specifically, session data exhibits some unique characteristics, i.e., session consistency and sequential dependency over items within the session, repeated item consumption, and session timeliness. In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. The comprehensive nature of our models helps improve the quality of session-based recommendation. More importantly, it provides a generalized framework for reflecting different perspectives of session data. Furthermore, since our models can be solved by closed-form solutions, they are highly scalable. Experimental results demonstrate that the proposed linear models show competitive or state-of-the-art performance in various metrics on several real-world datasets.

Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model. Despite the success of KD in the classification task, applying KD to recommender models is challenging due to the sparsity of positive feedback, the ambiguity of missing feedback, and the ranking problem associated with the top-N recommendation. To address the issues, we propose a new KD model for the collaborative filtering approach, namely collaborative distillation (CD). Specifically, (1) we reformulate a loss function to deal with the ambiguity of missing feedback. (2) We exploit probabilistic rank-aware sampling for the top-N recommendation. (3) To train the proposed model effectively, we develop two training strategies for the student model, called the teacher-and the student-guided training methods, selecting the most useful feedback from the teacher model. Via experimental results, we demonstrate that the proposed model outperforms the state-of-the-art method by 5.5-29.7% and 4.8-27.8% in hit rate (HR) and normalized discounted cumulative gain (NDCG), respectively. Moreover, the proposed model achieves the performance comparable to the teacher model.

Implicit user feedback is a fundamental dataset for personalized recommendation models. Because of its inherent characteristics of sparse one-class values, it is challenging to uncover meaningful user/item representations. In this paper, we propose dual neural personalized ranking (DualNPR), which fully exploits both user- and item-side pairwise rankings in a unified manner. The key novelties of the proposed model are three-fold: (1) DualNPR discovers mutual correlation among users and items by utilizing both user- and item-side pairwise rankings, alleviating the data sparsity problem. We stress that, unlike existing models that require extra information, DualNPR naturally augments both user- and item-side pairwise rankings from a user-item interaction matrix. (2) DualNPR is built upon deep matrix factorization to capture the variability of user/item representations. In particular, it chooses raw user/item vectors as an input and learns latent user/item representations effectively. (3) DualNPR employs a dynamic negative sampling method using an exponential function, further improving the accuracy of top-N recommendation. In experimental results over three benchmark datasets, DualNPR outperforms baseline models by 21.9-86.7% in hit rate, 14.5-105.8% in normalized discounted cumulative gain, and 5.1-23.3% in the area under the ROC curve.