Contrastive Meta Learning with Behavior Multiplicity for Recommendation (2202.08523v1)

Abstract: A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, social media). Traditional recommendation models usually assume that only a single type of interaction exists between user and item, and fail to model the multiplex user-item relationships from multi-typed user behavior data, such as page view, add-to-favourite and purchase. While some recent studies propose to capture the dependencies across different types of behaviors, two important challenges have been less explored: i) Dealing with the sparse supervision signal under target behaviors (e.g., purchase). ii) Capturing the personalized multi-behavior patterns with customized dependency modeling. To tackle the above challenges, we devise a new model CML, Contrastive Meta Learning (CML), to maintain dedicated cross-type behavior dependency for different users. In particular, we propose a multi-behavior contrastive learning framework to distill transferable knowledge across different types of behaviors via the constructed contrastive loss. In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users. Extensive experiments on three real-world datasets indicate that our method consistently outperforms various state-of-the-art recommendation methods. Our empirical studies further suggest that the contrastive meta learning paradigm offers great potential for capturing the behavior multiplicity in recommendation. We release our model implementation at: https://github.com/weiwei1206/CML.git.

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

The paper "Contrastive Meta Learning with Behavior Multiplicity for Recommendation" presents a novel approach to enhancing recommendation systems through a framework called Contrastive Meta Learning (CML). This approach is particularly focused on leveraging multiple types of user behaviors within a recommendation context to improve prediction accuracy, addressing some key challenges in multi-behavior recommendation systems that have been less explored.

The authors identify two main challenges in capturing multi-typed user-item interactions: sparse supervision signals under target behaviors (such as purchase activities) and the need to capture personalized multi-behavior patterns with customized dependency modeling. Traditional recommendation models tend to oversimplify user-item interactions by assuming a singular type of interaction, ignoring the nuanced dependencies that exist in multi-typed behavior scenarios.

CML aims to tackle these challenges by introducing a contrastive learning framework that effectively distills knowledge across different behavior types and enhances behavior embeddings via a contrastive loss. The model constructs behavior-aware views and uses contrastive learning to maximize the agreement between representations from different behavior types, which provides additional sources of supervision. The approach emphasizes the personalized nature of user interactions, enabling the framework to maintain dedicated representations through a contrastive meta network, which is designed to capture these heterogeneous dependencies.

The paper reports extensive empirical validation over three real-world datasets: Tmall, IJCAI-Contest, and Retail Rocket. The experimental results demonstrate that CML consistently outperforms several state-of-the-art methods, including both single-behavior recommendation models (such as BPR and LightGCN) and multi-behavior models (such as MATN, MBGCN, and KHGT), with improvements in HR@10 and NDCG@10 being as significant as 25.4% in certain cases.

The implications of this research are multi-fold. Practically, it shows that leveraging contrastive learning paradigms could provide substantial improvements for applications in personalized recommendations, especially in situations where certain types of user data are sparse. Theoretically, the idea of incorporating meta-learning to create personalized contrastive learning tasks opens new avenues for research in recommendation systems and beyond, hinting at the potential of personalized self-supervised learning tasks in other domains where adaptiveness to user-specific patterns is crucial.

Future developments could include exploring other meta-learning techniques to optimize the weighting functions used in contrastive learning or expanding the framework to accommodate dynamic or real-time data, which could further improve recommendation accuracy and user satisfaction. Moreover, integrating such a model into a larger recommendation ecosystem could demonstrate its robustness and scalability in commercial settings. As the model learns to adaptively integrate multiple behavior signals, it becomes a promising candidate for applications that require nuanced understanding and forecasting of user behavior, such as targeted advertising and personalized content delivery.