Advances in Efficient and Scalable Tucker Decomposition for Sequential Recommendation

In this article, we propose a new approach to sequential recommendation called S3-Rec, which stands for Self-supervised learning for Sequential Recommendation with Mutual Information Maximization. S3-Rec is designed to capture long-term user behavior patterns while only considering a small number of recent interactions.
Imagine you’re planning a trip and want to recommend some hotels to a friend based on their previous travel history. Traditional recommendation systems might only consider the last few hotels they stayed at, but S3-Rec takes into account all the hotels they’ve ever visited, even if it was years ago. This allows it to provide more accurate and personalized recommendations.
S3-Rec works by representing user behavior as a tensor model, which is similar to a multi-dimensional matrix. This model assigns scores to each interaction between a user and an item, indicating how relevant the interaction is to the user’s past behavior. By maximizing the mutual information between these scores, S3-Rec can learn long-term patterns in user behavior without requiring explicit labels or supervision.
Another key innovation of S3-Rec is its ability to handle high-dimensional item catalogs by using a compact representation called Hankel matrix. This allows S3-Rec to efficiently process large datasets while avoiding computational complexity issues.
We evaluate the performance of S3-Rec on several real-world datasets and compare it to state-of-the-art sequential recommendation models. Our results show that S3-Rec outperforms these models in terms of both accuracy and efficiency, demonstrating its effectiveness in capturing long-term user behavior patterns.
Overall, S3-Rec represents a significant advancement in the field of sequential recommendation systems, enabling more accurate and personalized recommendations while reducing computational complexity. Its ability to handle high-dimensional item catalogs makes it particularly useful for real-world applications, where datasets can be large and complex.