Co-learning multiple browsing tendencies of a user by matrix factorization-based multitask learning

Guo Jhen Bai, Cheng You Lien, Hung Hsuan Chen

研究成果: 書貢獻/報告類型會議論文篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

Predicting an online user’s future behavior is beneficial for many applications. For example, online retailers may utilize such information to customize the marketing strategy and maximize profit. This paper aims to predict the types of webpages a user is going to click on. We observe that instead of building independent models to predict each individual type of web page, it is more effective to use a unified model to predict a user’s future clicks on different types of web pages simultaneously. The proposed model makes predictions based on the latent variables that represent possible interactions among the multiple targets and among the features. The experimental results show that this method outperforms the carefully tuned single-target training models most of the time. If the size of the training data is limited, the model shows a significant improvement over the baseline models, likely because the hidden relationship among different targets can be discovered by our model.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
編輯Payam Barnaghi, Georg Gottlob, Yannis Manolopoulos, Theodoros Tzouramanis, Athena Vakali
發行者Association for Computing Machinery, Inc
頁面253-257
頁數5
ISBN(電子)9781450369343
DOIs
出版狀態已出版 - 14 10月 2019
事件19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019 - Thessaloniki, Greece
持續時間: 13 10月 201917 10月 2019

出版系列

名字Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019

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???event.eventtypes.event.conference???19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
國家/地區Greece
城市Thessaloniki
期間13/10/1917/10/19

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