SSE-PT: Sequential Recommendation Via Personalized Transformer

By Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack. ACM Conference on Recommender Systems, 2020

307 citations recorded by the source corpus.

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/wuliwei9278/SSE-PT.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5aed438fecf702ead9c0cddfa9a80bd00cff0138

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Xiangnan He
  2. Xiang Wang
  3. Meng Wang
  4. Le Wu
  5. Kun Zhang
  6. Tat-seng Chua
  7. Liqiang Nie
  8. Fuli Feng

In Information Systems

  1. Xiang Wang
  2. Xiangnan He
  3. Meng Wang
  4. Le Wu
  5. Kun Zhang
  6. Tat-seng Chua
  7. Liqiang Nie
  8. Fuli Feng

In Management Science and Operations Research

  1. Xiang Wang
  2. Meng Wang
  3. Xiangnan He
  4. Le Wu
  5. Kun Zhang
  6. Tat-seng Chua
  7. Fuli Feng

Citations in the corpus