Sequence-aware factorization machines for temporal predictive analytics

Tong Chen, Hongzhi Yin*, Quoc Viet Hung Nguyen, Wen Chih Peng, Xue Li, Xiaofang Zhou

*Corresponding author for this work

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics. As the volume of web-scale data grows exponentially over time, sparse predictive analytics inevitably involves dynamic and sequential features. However, existing FM-based models assume no temporal orders in the data, and are unable to capture the sequential dependencies or patterns within the dynamic features, impeding the performance and adaptivity of these methods. Hence, in this paper, we propose a novel Sequence-Aware Factorization Machine (SeqFM) for temporal predictive analytics, which models feature interactions by fully investigating the effect of sequential dependencies. As static features (e.g., user gender) and dynamic features (e.g., user interacted items) express different semantics, we innovatively devise a multi-view self-attention scheme that separately models the effect of static features, dynamic features and the mutual interactions between static and dynamic features in three different views. In SeqFM, we further map the learned representations of feature interactions to the desired output with a shared residual network. To showcase the versatility and generalizability of SeqFM, we test SeqFM in three popular application scenarios for FM-based models, namely ranking, classification and regression tasks. Extensive experimental results on six large-scale datasets demonstrate the superior effectiveness and efficiency of SeqFM.

原文English
主出版物標題Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
發行者IEEE Computer Society
頁面1405-1416
頁數12
ISBN(電子)9781728129037
DOIs
出版狀態Published - 四月 2020
事件36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
持續時間: 20 四月 202024 四月 2020

出版系列

名字Proceedings - International Conference on Data Engineering
2020-April
ISSN(列印)1084-4627

Conference

Conference36th IEEE International Conference on Data Engineering, ICDE 2020
國家United States
城市Dallas
期間20/04/2024/04/20

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