Shi, Zhengxiang;
Wang, Xi;
Lipani, Aldo;
(2024)
Self Contrastive Learning for Session-Based Recommendation.
In: Goharian, Nazli and Tonellotto, Nicola and He, Yulan and Lipani, Aldo and McDonald, Graham and Macdonald, Craig and Ounis, Iadh, (eds.)
Advances in Information Retrieval (ECIR 2024).
(pp. pp. 3-20).
Springer: Cham, Switzerland.
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Abstract
Session-based recommendation, which aims to predict the next item of users’ interest as per an existing sequence interaction of items, has attracted growing applications of Contrastive Learning (CL) with improved user and item representations. However, these contrastive objectives: (1) serve a similar role as the cross-entropy loss while ignoring the item representation space optimisation; and (2) commonly require complicated modelling, including complex positive/negative sample constructions and extra data augmentation. In this work, we introduce Self-Contrastive Learning (SCL), which simplifies the application of CL and enhances the performance of state-of-the-art CL-based recommendation techniques. Specifically, SCL is formulated as an objective function that directly promotes a uniform distribution among item representations and efficiently replaces all the existing contrastive objective components of state-of-the-art models. Unlike previous works, SCL eliminates the need for any positive/negative sample construction or data augmentation, leading to enhanced interpretability of the item representation space and facilitating its extensibility to existing recommender systems. Through experiments on three benchmarks, we demonstrate that SCL consistently improves the performance of state-of-the-art models with statistical significance. Notably, our experiments show that SCL improves the performance of two best-performing models by 8.2% and 9.5% in P@10 (Precision) and 9.9% and 11.2% in MRR@10 (Mean Reciprocal Rank) on average across different benchmarks. Additionally, our analysis elucidates the improvement in terms of alignment and uniformity of representations, as well as the effectiveness of SCL with a low computational cost. Code is available at https://github.com/ZhengxiangShi/SelfContrastiveLearningRecSys.
| Type: | Proceedings paper |
|---|---|
| Title: | Self Contrastive Learning for Session-Based Recommendation |
| Event: | 46th European Conference on Information Retrieval, ECIR 2024 |
| Location: | Glasgow, UK |
| Dates: | 24 Mar 2024 - 28 Mar 2024 |
| ISBN-13: | 978-3-031-56026-2 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-56027-9_1 |
| Publisher version: | https://doi.org/10.1007/978-3-031-56027-9_1 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Recommendation System; Session-based Recommendation; Contrastive Learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10193553 |
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