LSTM-based recommendation approach for interaction records

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

Interactive platforms such as Spotify and Steam currently play an increasingly important role on the Internet. Users continuously use the content on these platforms. Therefore, the most important data in interactive platforms are interaction records, which contain an enormous amount of information regarding user interests at any given time. However, previous recommendation approaches have been unable to process such records satisfactorily. Therefore, we propose an LSTM-based recommendation approach for interaction records. In our approach, we used a recurrent neural network (RNN) based on LSTM to make recommendations by learning user interests and their changing trend. We propose a pretreatment called serial filling at equal ratio to apply LSTM. Further, we used a dimensionality reduction technique based on matrix factorization to improve the system efficiency. Finally, we evaluated our approach using Steam datasets. As indicated by the results, our approach performs better than other conventional approaches in three aspects: Accuracy, efficiency, and diversity.

本文言語英語
ホスト出版物のタイトルProceedings of the 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019
編集者Sukhan Lee, Hyunseung Choo, Roslan Ismail
出版社Springer Verlag
ページ950-962
ページ数13
ISBN(印刷版)9783030190620
DOI
出版ステータス出版済み - 1 1 2019
イベント13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019 - Phuket, タイ
継続期間: 1 4 20191 6 2019

出版物シリーズ

名前Advances in Intelligent Systems and Computing
935
ISSN(印刷版)2194-5357
ISSN(電子版)2194-5365

会議

会議13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019
国/地域タイ
CityPhuket
Period1/4/191/6/19

All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • コンピュータ サイエンス(全般)

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