Temporal Collaborative Ranking Via Personalized Transformer (1908.05435v1)

Abstract: The collaborative ranking problem has been an important open research question as most recommendation problems can be naturally formulated as ranking problems. While much of collaborative ranking methodology assumes static ranking data, the importance of temporal information to improving ranking performance is increasingly apparent. Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed us to make better use of the temporal ordering of items that each user has engaged with. In particular, the SASRec model, inspired by the popular Transformer model in natural languages processing, has achieved state-of-art results in the temporal collaborative ranking problem and enjoyed more than 10x speed-up when compared to earlier CNN/RNN-based methods. However, SASRec is inherently an un-personalized model and does not include personalized user embeddings. To overcome this limitation, we propose a Personalized Transformer (SSE-PT) model, outperforming SASRec by almost 5% in terms of NDCG@10 on 5 real-world datasets. Furthermore, after examining some random users' engagement history and corresponding attention heat maps used during the inference stage, we find our model is not only more interpretable but also able to focus on recent engagement patterns for each user. Moreover, our SSE-PT model with a slight modification, which we call SSE-PT++, can handle extremely long sequences and outperform SASRec in ranking results with comparable training speed, striking a balance between performance and speed requirements. Code and data are open sourced at https://github.com/wuliwei9278/SSE-PT.

• The paper introduces SSE-PT, a novel transformer model that integrates personalized user embeddings with temporal dynamics for enhanced collaborative ranking.
• The methodology employs Stochastic Shared Embeddings to regularize additional parameters, effectively reducing overfitting while boosting model efficiency.
• Empirical results show nearly a 5% increase in NDCG@10 across varied datasets, underscoring SSE-PT's robust performance in recommendation systems.

An Academic Overview of "Temporal Collaborative Ranking Via Personalized Transformer"

The paper in question presents an advancement in the domain of collaborative ranking for recommendation systems, particularly emphasizing the integration of temporal dynamics through a novel Personalized Transformer model (SSE-PT). This research is situated in the broader effort to optimize ranking performance in recommendation systems by leveraging the temporal order of user-item interactions, a factor that has been increasingly recognized for its potential to refine predictive accuracy.

Problem Context and Methodology

The challenge addressed in the paper is aptly termed the Temporal Collaborative Ranking problem. Traditional collaborative ranking models have fallen short in not adequately incorporating the dynamism introduced by temporal dependencies, which are crucial as user preferences can evolve significantly over time. The authors critique existing models like SASRec, which, while efficient and state-of-the-art in some respects, fail to personalize across diverse user embeddings, potentially leading to suboptimal ranking results.

The introduction of the SSE-PT model signifies a marked shift in addressing these shortcomings. By embedding user personalization directly into the transformer architecture—specifically through the integration of user embeddings along with temporal data—this model asserts a notable improvement over its predecessors, boasting a near 5% enhancement in NDCG@10 across five real-world datasets when compared to SASRec.

Empirical Validation and Results

The empirical results presented in the paper yield compelling evidence for SSE-PT's superiority. When benchmarked against both classical and modern ranking approaches, SSE-PT consistently outperforms in terms of key ranking metrics such as NDCG and Recall. The datasets utilized—Beauty, Games, Steam, Movielens1M, and Movielens10M—offer a comprehensive testing ground, encapsulating varying degrees of sparse and dense interactions, sequence lengths, and domain-specific challenges.

Innovations and Implications

A standout innovation in this work is the implementation of the Stochastic Shared Embeddings (SSE), a regularization technique that significantly mitigates the risk of overfitting—a recurring challenge when introducing additional parameters like user embeddings in complex models. This regularization strategy allows the SSE-PT to retain predictive power while maintaining computational efficiency.

Additionally, the SSE-PT++ variant is introduced, designed to handle longer interaction sequences without sacrificing model efficiency. This adaptability is crucial in handling datasets like Movielens10M with extensive engagement histories, highlighting the model's robustness across varying temporal scales.

Theoretical and Practical Implications

Theoretically, this work contributes to the literature by demonstrating the efficacy of personalized embeddings within transformer architectures for recommendation systems. The model's ability to provide focused and tailored recommendations aligns with emerging trends in user-centric AI applications, where personalization is paramount.

Practically, the enhanced interpretability of the attention mechanisms within SSE-PT—taking into account recent engagement patterns—offers tangible benefits for practitioners looking to deploy more intuitive recommendation systems in commercial settings.

Future Directions

Looking ahead, the implications of this research beckon further exploration into enhancing personalization in transformers, particularly through more sophisticated embeddings and hybrid architectures that combine with other machine learning paradigms. Additionally, investigating the scalability of SSE-PT in even larger-scale systems and its adaptability across diverse application domains could cement its utility in future AI deployments.

In conclusion, this paper makes significant strides in the field of recommendation systems, offering a methodologically sound and practically relevant solution to temporal collaborative ranking through the Personalized Transformer model. This work not only sets a benchmark for future research but also provides a clear pathway for practical application in industry.