Temporal information is crucial for recommendation problems because user preferences are naturally dynamic in the real world. 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 for 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-the-art results. However, SASRec, just like the original Transformer model, 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, we find our model 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. Our novel application of the Stochastic Shared Embeddings (SSE) regularization is essential to the success of personalization. Code and data are open-sourced at https://github.com/SSE-PT/SSE-PT.
The paper proposes SSE-PT for sequential recommendation, which is an extension of previous work SASRec by adding user embedding with SSE  regularization [Wu et al. 2019] . They further extend SSE-PT to SSE-PT++ to handle longer sequence. Experiments on five datasets show that the SSE-PT and SSE-PT++ outperform several baseline approaches.
Detailed comments:
1)	The technical contribution seems to be scattered: user embedding is introduced, effect of different types of regularization is studied and sampling based approach is added to address long sequence. It could be better if the author could make clear what the major contribution of this paper is. Also, SSE [Wu et al. 2019] is existing technique and simply applying it to sequential recommendation is a bit incremental.
2)    In addition to SASRec, there are some other transformer based model (e.g., [1]) for sequential recommendation and the paper discuss how the proposed method differ from them.
3)	In SSE-PT++, would sampling start index v based on the recency (e.g., with exponential decay) make more sense than uniform probability?
4） Overall, experiments look comprehensive: The baseline methods include both non-deep-learning methods and recent deep learning based methods for sequential recommendation; ablation study is conducted; case study is performed on MovieLens to show how the attention weights differ from SASRec; running time is compared against baselines and sensitivity analysis on hyper-parameters are also provided. 
To summarize, the paper is a bit incremental/scattered in terms of technical contribution but the execution of this paper looks solid. I would give a “weak accept” to this paper given the reasons listed above.
[1] F. Sun et. al. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer