Multimodal sparse linear integration for content-based item recommendation

Qiusha Zhu, Zhao Li, Haohong Wang, Yimin Yang, Mei Ling Shyu

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

7 Scopus citations

Abstract

Most content-based recommender systems focus on analyzing the textual information of items. For items with images, the images can be treated as another information modality. In this paper, an effective method called MSLIM is proposed to integrate multimodal information for content-based item recommendation. It formalizes the probelm into a regularized optimization problem in the least-squares sense and the coordinate gradient descent is applied to solve the problem. The aggregation coefficients of the items are learned in an unsupervised manner during this process, based on which the k-nearest neighbor (k-NN) algorithm is used to generate the top-N recommendations of each item by finding its k nearest neighbors. A framework of using MSLIM for item recommendation is proposed accordingly. The experimental results on a self-collected handbag dataset show that MSLIM outperforms the selected comparison methods and show how the model parameters affect the final recommendation results.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Symposium on Multimedia, ISM 2013
Pages187-194
Number of pages8
DOIs
StatePublished - Dec 1 2013
Event15th IEEE International Symposium on Multimedia, ISM 2013 - Anaheim, CA, United States
Duration: Dec 9 2013Dec 11 2013

Publication series

NameProceedings - 2013 IEEE International Symposium on Multimedia, ISM 2013

Other

Other15th IEEE International Symposium on Multimedia, ISM 2013
CountryUnited States
CityAnaheim, CA
Period12/9/1312/11/13

Keywords

  • multimodal integration
  • Recommendation
  • sparse linear

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

Fingerprint Dive into the research topics of 'Multimodal sparse linear integration for content-based item recommendation'. Together they form a unique fingerprint.

  • Cite this

    Zhu, Q., Li, Z., Wang, H., Yang, Y., & Shyu, M. L. (2013). Multimodal sparse linear integration for content-based item recommendation. In Proceedings - 2013 IEEE International Symposium on Multimedia, ISM 2013 (pp. 187-194). [6746789] (Proceedings - 2013 IEEE International Symposium on Multimedia, ISM 2013). https://doi.org/10.1109/ISM.2013.37