An Online Activity Recommendation Approach Based on the Dynamic Adjustment of Recommendation Lists

Duen-Ren Liu, Kuan Yu Chen, Yun Cheng Chou, Jia Huei Lee

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

3 Scopus citations

Abstract

This research investigates an online recommendation method for new types of online news websites. Cross-domain analysis on user browsing news and the attending activities is conducted to predict user preferences on activities based on nonnegative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) topic model. A novel approach is proposed for the dynamic adjustment of recommendation lists in order to tackle the issue of limited recommendation layouts. The existing studies have not addressed this issue. The proposed approach is implemented on an online news website and evaluated for online recommendations. The experiment results demonstrate that our method can predict user preferences on recommended activities and enhance the effectiveness of recommendations.

Original languageEnglish
Title of host publicationProceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
EditorsKiyota Hashimoto, Naoki Fukuta, Tokuro Matsuo, Sachio Hirokawa, Masao Mori, Masao Mori
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages407-412
Number of pages6
ISBN (Electronic)9781538606216
DOIs
StatePublished - 15 Nov 2017
Event6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 - Hamamatsu, Shizuoka, Japan
Duration: 9 Jul 2017 → …

Publication series

NameProceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017

Conference

Conference6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
CountryJapan
CityHamamatsu, Shizuoka
Period9/07/17 → …

Keywords

  • Data Mining
  • Dynamic Adjustment of Recommendation List
  • Latent Topic Model
  • Matrix Factorization
  • Online Recommendation
  • Recommender system

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