Deep neural networks improved the accuracy of sequential recommendation approach which takes into account the sequential patterns of user logs, e.g., a purchase history of a user. However, incorporating only the individual's recent logs may not be sufficient in properly reflecting global preferences and trends across all users and items. In response, we propose a self-attentive sequential recommender system with topic modeling-based category embedding as a novel approach to exploit global information in the process of sequential recommendation. Our self-attention module effectively leverages the sequential patterns from the user's recent history. In addition, our novel category embedding approach, which utilizes the information computed by topic modeling, efficiently captures global information that the user generally prefers. Furthermore, to provide diverse recommendations as well as to prevent overfitting, our model also incorporates a vector obtained by random sampling. Experimental studies show that our model outperforms state-of-the-art sequential recommendation models, and that category embedding effectively provides global preference information.
|Title of host publication||CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|Publication status||Published - 2019 Nov 3|
|Event||28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China|
Duration: 2019 Nov 3 → 2019 Nov 7
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||28th ACM International Conference on Information and Knowledge Management, CIKM 2019|
|Period||19/11/3 → 19/11/7|
Bibliographical noteFunding Information:
This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2017M3C4A7063570).
© 2019 Association for Computing Machinery.
- Deep neural networks
- Recommender system
- Topic modeling
ASJC Scopus subject areas
- Decision Sciences(all)
- Business, Management and Accounting(all)