Document recommendation with implicit feedback based on matrix factorization and topic model

Chin Hui Lai, Duen-Ren Liu, Siao Rong Lin

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

Recommender systems have been applied in many domains to solve the information-overload problem, and most of them make recommendations based on explicit data which expressed ratings in different scores. However, there are a lot of implicit data in the real world, such as users' purchase history, click history, browsing activity and so on, and it is difficult to find users' preferences based on this kind of data. In this work, we proposed a novel recommendation method, which incorporates topic model and matrix factorization. The content of documents and similar users' preferences are used to predict the negative and positive examples. The proposed approach achieves better performance than other recommender systems with implicit feedback.

原文English
主出版物標題Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
編輯Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面62-65
頁數4
ISBN(電子)9781538643426
DOIs
出版狀態Published - 22 6月 2018
事件4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, 日本
持續時間: 13 4月 201817 4月 2018

出版系列

名字Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

Conference

Conference4th IEEE International Conference on Applied System Innovation, ICASI 2018
國家/地區日本
城市Chiba
期間13/04/1817/04/18

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