S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (2008.07873v1)

Abstract: Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation. To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture. The main idea of our approach is to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence by utilizing the mutual information maximization (MIM) principle. MIM provides a unified way to characterize the correlation between different types of data, which is particularly suitable in our scenario. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available. Besides, we extend our self-supervised learning method to other recommendation models, which also improve their performance.

• The paper introduces a novel S^3-Rec model that enriches item representations by integrating auxiliary self-supervision signals.
• The paper employs mutual information maximization across data segments to improve robustness in sparse recommendation scenarios.
• The paper demonstrates significant performance gains, with enhanced Hit Ratio and NDCG metrics on six diverse real-world datasets.

Self-Supervised Learning for Sequential Recommendation: An Analysis of the S$^3$-Rec Model

This essay presents an expert analysis of the paper titled "S$^3$-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization." The work introduces a novel self-supervised approach to enhance sequential recommendation systems through mutual information maximization.

Core Contributions

1. Self-Supervised Learning in Recommendation: The paper proposes a model, S$^3$-Rec, which leverages self-supervised learning to tackle data sparsity in sequential recommendation models. Unlike traditional models that rely solely on item prediction losses, S$^3$-Rec integrates auxiliary self-supervision signals, enriching data representation through pre-trained methods.
2. Mutual Information Maximization (MIM): The authors utilize MIM to exploit the correlation between different data types. By maximizing mutual information among attributes, items, sub-sequences, and sequences, the model enhances its ability to learn more robust data representations.
3. Pre-Training and Fine-Tuning Strategy: S$^3$-Rec employs a two-stage learning strategy. The pre-training phase enhances data representations with self-supervised signals. The fine-tuning stage optimizes the model for specific recommendation tasks, leveraging the enriched representations.
4. Empirical Validity: Extensive experiments on six real-world datasets demonstrate S$^3$-Rec's superiority over state-of-the-art methods, emphasizing its effectiveness in scenarios with limited training data.

Methodology

The authors present a self-attentive neural architecture, capitalizing on its suitability for sequential data patterns. Four auxiliary self-supervised objectives, namely Associated Attribute Prediction (AAP), Masked Item Prediction (MIP), Masked Attribute Prediction (MAP), and Segment Prediction (SP), are devised. Each objective helps in capturing different granularity levels or forms of data, ensuring comprehensive data utilization. The integration of self-supervision signals provides an improved basis for subsequent fine-tuning, ultimately enhancing recommendation quality.

Results and Implications

The experimental setup incorporates six diverse datasets, showcasing the model's robustness across different domains and sparsity levels. Key metrics such as Hit Ratio and NDCG demonstrate significant improvements over baselines like GRU4Rec, SASRec, and FDSA. S$^3$-Rec's resilience in sparse data environments is particularly noteworthy, suggesting its applicability in real-world applications where data is often sparse.

Future Directions

The authors speculate that exploring additional self-supervised objectives and expanding the approach to more complex recommendation scenarios, like conversational or multimedia recommendations, could unlock further potential. The successful application of MIM indicates promising avenues for future research in representational learning within recommendation systems.

Conclusion

S$^3$-Rec underscores the potential of self-supervised learning in overcoming inherent challenges in sequential recommendation tasks. The innovative use of mutual information maximization enriches the field's understanding of data correlation exploitation in enhancing model performance. As AI continues to evolve, techniques demonstrated in this paper will likely inspire further advancements in self-supervised methodologies within various AI applications.