LSTM-based recommendation approach for interaction records

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019
EditorsSukhan Lee, Hyunseung Choo, Roslan Ismail
PublisherSpringer Verlag
Pages950-962
Number of pages13
ISBN (Print)9783030190620
DOIs
Publication statusPublished - Jan 1 2019
Event13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019 - Phuket, Thailand
Duration: Jan 4 2019Jan 6 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume935
ISSN (Print)2194-5357

Conference

Conference13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019
CountryThailand
CityPhuket
Period1/4/191/6/19

Fingerprint

Steam
Recurrent neural networks
Factorization
Internet

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Zhou, Y., & Ushiama, T. (2019). LSTM-based recommendation approach for interaction records. In S. Lee, H. Choo, & R. Ismail (Eds.), Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019 (pp. 950-962). (Advances in Intelligent Systems and Computing; Vol. 935). Springer Verlag. https://doi.org/10.1007/978-3-030-19063-7_74

LSTM-based recommendation approach for interaction records. / Zhou, Yan; Ushiama, Taketoshi.

Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019. ed. / Sukhan Lee; Hyunseung Choo; Roslan Ismail. Springer Verlag, 2019. p. 950-962 (Advances in Intelligent Systems and Computing; Vol. 935).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhou, Y & Ushiama, T 2019, LSTM-based recommendation approach for interaction records. in S Lee, H Choo & R Ismail (eds), Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019. Advances in Intelligent Systems and Computing, vol. 935, Springer Verlag, pp. 950-962, 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019, Phuket, Thailand, 1/4/19. https://doi.org/10.1007/978-3-030-19063-7_74
Zhou Y, Ushiama T. LSTM-based recommendation approach for interaction records. In Lee S, Choo H, Ismail R, editors, Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019. Springer Verlag. 2019. p. 950-962. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-19063-7_74
Zhou, Yan ; Ushiama, Taketoshi. / LSTM-based recommendation approach for interaction records. Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019. editor / Sukhan Lee ; Hyunseung Choo ; Roslan Ismail. Springer Verlag, 2019. pp. 950-962 (Advances in Intelligent Systems and Computing).
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