@inproceedings{b92200768e7342a683122e492e62430b,
title = "CARE: Learning convolutional attentional recurrent embedding for sequential recommendation",
abstract = "Top-N sequential recommendation is to predict the next few items based on user's sequential interactions with past items. This paper aims at boosting the performance of top-N sequential recommendation based on a state-of-the-art model, Caser. We point out three insufficiencies of Caser - do not model variant-sized sequential patterns, treating the impact of each past time step equally, and cannot learn cumulative features. Then we propose a novel Convolutional Attentional Recurrent Embedding (CARE) learning model. Experiments conducted on a large-scale user-location check-in dataset exhibit promising performance, comparing to Caser.",
author = "Tsai, {Yu Che} and Li, {Cheng Te}",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 ; Conference date: 08-11-2021",
year = "2021",
month = nov,
day = "8",
doi = "10.1145/3487351.3489478",
language = "English",
series = "Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021",
publisher = "Association for Computing Machinery, Inc",
pages = "654--660",
editor = "Michele Coscia and Alfredo Cuzzocrea and Kai Shu",
booktitle = "Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021",
}