| Title | Accelerating DIN Model for Online CTR Prediction with Data Compression |
| Creator | |
| Date Issued | 2022 |
| Source Publication | 2022 7th International Conference on Big Data Analytics, ICBDA 2022
 |
| Pages | 84-89 |
| Abstract | As the key task of recommender systems, the click-Through rate(CTR) prediction is to predict the probability of users clicking on a specific product. It is often costly due to the big data sets. In this paper, we apply some data compression technology to accelerate CTR prediction. By casting the data format to some more memory-efficient format, we can significantly improve a popular recommender method online. |
| Keyword | CTR prediction data compression recommender system |
| DOI | 10.1109/ICBDA55095.2022.9760313 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85129526069 |
| Citation statistics | |
| Document Type | Conference paper |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/11499 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author | Zhu，Shengxin |
| Affiliation | 1.BNU-HKBU United International College,Division of Science and Technology,Zhuhai,China 2.Beijing Normal University,Research Center for Mathematics,Zhuhai,China |
| First Author Affilication | Beijing Normal-Hong Kong Baptist University |
| Recommended Citation GB/T 7714 | Feng，Yitian,Zhu，Shengxin,Ou，Yichen. Accelerating DIN Model for Online CTR Prediction with Data Compression[C], 2022: 84-89. |
| Files in This Item: | There are no files associated with this item. | |||||
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