Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation

Sequential recommendation addresses the issue of preference drift by predicting the next item based on the user's previous behaviors. Recently, a promising approach using contrastive learning has emerged, demonstrating its effectiveness in recommending items under sparse user-item interactions. Significantly, the effectiveness of combinations of various augmentation methods has been demonstrated in different domains, particularly in computer vision. However, when it comes to augmentation within a contrastive learning framework in sequential recommendation, previous research has only focused on limited conditions and simple structures. Thus, it is still possible to extend existing approaches to boost the effects of augmentation methods by using progressed structures with the combinations of multiple augmentation methods. In this work, we propose a novel framework called Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation(HCLRec) to overcome the aforementioned limitation. Our framework leverages existing augmentation methods hierarchically to improve performance. By combining augmentation methods continuously, we generate low-level and high-level view pairs. We employ a Transformers-based model to encode the input sequence effectively. Furthermore, we introduce additional blocks consisting of Transformers and position-wise feed-forward network(PFFN) layers to learn the invariance of the original sequences from hierarchically augmented views. We pass the input sequence to subsequent layers based on the number of increment levels applied to the views to handle various augmentation levels. Within each layer, we compute contrastive loss between pairs of views at the same level. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art approaches and that HCLRec is robust even when faced with the problem of sparse interaction.

A Differentiable Ranking Metric Using Relaxed Sorting Opeartion for Top-K Recommender Systems

A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering the top-Kitemswith high scores. While sorting and ranking items are integral for this recommendation procedure,it is nontrivial to incorporate them in the process of end-to-end model training since sorting is non-differentiable and hard to optimize with gradient-based updates. This incurs the inconsistency issue between the existing learning objectives and ranking-based evaluation metrics of recommendation models. In this work, we present DRM (differentiable ranking metric) that mitigates the inconsistency and improves recommendation performance, by employing the differentiable relaxation of ranking-based evaluation metrics. Via experiments with several real-world datasets, we demonstrate that the joint learning of the DRM cost function upon existing factor based recommendation models significantly improves the quality of recommendations, in comparison with other state-of-the-art recommendation methods.

A Differntiable Ranking Metric Using Relaxed Sorting Opeartor for Top-K Recommender Systems

A recommender system recommends a few items for a user by sorting items according to their predicted preferences and filter items with the highest predicted preferences. While sorting and selecting top-K items are an inherent part of the personalized recommendation, it is nontrivial to incorporate them in the process of end-to-end model training since sorting is not differentiable and impossible to optimize with gradient based updates. Instead, existing recommenders optimize surrogate objectives, often rendering suboptimal quality of recommendations. In this paper, we propose the differentiable ranking metrics (DRM), a differentiable relaxation of evaluation metrics such as Precision and Recall. DRM maximizes the evaluation metrics for recommendation models directly. Via experiments with several real-world datasets, we demonstrate that the joint learning of the DRM cost function upon existing factor-based recommendation models improves the quality of recommendations significantly, in comparison with other state-of-the-art recommendation methods.