  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.
