Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling
| Autor: | Woo Young Kim, Kyeongpil Kang, Jaegul Choo, Junwoo Park, Hojung Choe |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Topic model
0209 industrial biotechnology business.industry Process (engineering) Mechanism (biology) Computer science 02 engineering and technology Overfitting Recommender system Machine learning computer.software_genre Preference 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | CIKM |
| DOI: | 10.1145/3357384.3358054 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|