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SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning

arXiv 2209.10807 ↗First posted 22 Sep 2022cs.IR cs.AIcited by 0

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Abstract (arXiv)

Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. The previous works have been modeling the session as a variable-length of a sequence of items and learning the representation of both individual items and the aggregated session. Recent research has applied graph neural networks with an attention mechanism to capture complicated item transitions and dependencies by modeling the sessions into graph-structured data. However, they still face fundamental challenges in terms of data and learning methodology such as sparse supervision signals and noisy interactions in sessions, leading to sub-optimal performance. In this paper, we propose SR-GCL, a novel contrastive learning framework for a session-based recommendation. As a crucial component of contrastive learning, we propose two global context enhanced data augmentation methods while maintaining the semantics of the original session. The extensive experiment results on two real-world E-commerce datasets demonstrate the superiority of SR-GCL as compared to other state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 22 Sep 2022.

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References · 24

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
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12 more
  1. 2015Generating Sentences from a Continuous Spacecited by 612
  2. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 520
  3. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
  4. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 370
  5. 2018Session-based Recommendation with Graph Neural Networkscited by 312
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  7. 2019A Survey on Session-based Recommender Systemscited by 97
  8. 2018Data Augmentation using Random Image Cropping and Patching for Deep CNNscited by 88
  9. 2019Rethinking the Item Order in Session-based Recommendation with Graph Neural Networkscited by 43
  10. 2016A Generic Coordinate Descent Framework for Learning from Implicit Feedbackcited by 16
  11. 2019NISER: Normalized Item and Session Representations to Handle Popularity Biascited by 12
  12. 2020Rethinking Item Importance in Session-based Recommendationcited by 7

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