@inproceedings{1faa48c9084042169e4f1adae4482f00,
title = "Modelling temporal dynamics and repeated behaviors for recommendation",
abstract = "Personalized recommendation has yield immense success in predicting user preference with heterogeneous implicit feedback (HIF), i.e., various user behaviors. However, existing studies consider less about the temporal dynamics and repeated patterns of HIF. They simply suppose: (1) a hard rule among user behaviors (e.g., add-to-cart must come before purchase and after view); (2) merge repeated behaviors into one (e.g., view several times is considered as view once only), thus failing to unveil user preferences from their real behaviors. To ease these issues, we, therefore, propose a novel end-to-end neural framework – TDRB, which automatically models the Temporal Dynamics and Repeated Behaviors to assist in capturing user preference, thus achieving more accurate recommendations. Empirical studies on three real-world datasets demonstrate the superiority of our proposed TDRB against other state-of-the-arts.",
keywords = "Temporal dynamics, Repeated behaviors, Heterogeneous implicit feedback, Recommendation",
author = "Xin Zhou and Zhu Sun and Guibing Guo and Yuan Liu",
year = "2020",
doi = "10.1007/978-3-030-47426-3_15",
language = "English",
isbn = "978-3-030-47425-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "181--193",
editor = "Lauw, {Hady W.} and Wong, {Raymond Chi-Wing} and Alexandros Ntoulas and Ee-Peng Lim and See-Kiong Ng and Pan, {Sinno Jialin}",
booktitle = "Advances in Knowledge Discovery and Data Mining",
address = "United States",
note = "24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 ; Conference date: 11-05-2020 Through 14-05-2020",
}