Sequential Recommendation with Relation-Aware Kernelized Self-Attention


  • Mingi Ji KAIST
  • Weonyoung Joo KAIST
  • Kyungwoo Song KAIST
  • Yoon-Yeong Kim KAIST
  • Il-Chul Moon KAIST



Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Transformer is a deterministic measure without relation-awareness. Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics. We experimented RKSA over the benchmark datasets, and RKSA shows significant improvements compared to the recent baseline models. Also, RKSA were able to produce a latent space model that answers the reasons for recommendation.




How to Cite

Ji, M., Joo, W., Song, K., Kim, Y.-Y., & Moon, I.-C. (2020). Sequential Recommendation with Relation-Aware Kernelized Self-Attention. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4304-4311.



AAAI Technical Track: Machine Learning