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Local Top-N Recommendation via Refined Item-User Bi-Clustering

  • Yuheng Wang
  • Xiang ZhaoEmail author
  • Yifan Chen
  • Wenjie Zhang
  • Weidong Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10570)

Abstract

Top-\(N\) recommendation has drawn much attention from many portal websites nowadays. The classic item-based methods based on sparse linear models (SLIM) have demonstrated very good performance, which estimate a single model for all users. Lately, local models have been considered necessary since a user only resembles a group of others but not all. Moreover, we find that two users with similar tastes on one item group may have totally different tastes on another. Thus, it is intuitive to make preference predictions for a user via item-user subgroups rather than the entire feedback matrix. For elegant local top-\(N\) recommendation, this paper introduces a bi-clustering scheme to be integrated with SLIM, such that item-user subgroups are softly constructed to capture subtle preferences of users. A novel localized recommendation model is hence presented, and an alternative direction algorithm is devised to collectively learn item coefficient for each local model. To deal with the data sparsity issue during clustering, we conceive a refined feature-based distance measure to better model and reflect user-item interaction. The proposed method is experimentally compared with state-of-the-art methods, and the results demonstrate the superiority of our model.

Notes

Acknowledgment

This work was partially supported by NSFC under grants Nos. 61402494, 61402498, 71690233, NSF Hunan under grant No. 2015JJ4009, and ARC under grants DP150103071, DP150102728.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuheng Wang
    • 1
  • Xiang Zhao
    • 1
    • 3
    Email author
  • Yifan Chen
    • 1
  • Wenjie Zhang
    • 2
  • Weidong Xiao
    • 1
    • 3
  1. 1.National University of Defense TechnologyChangshaChina
  2. 2.The University of New South WalesKensingtonAustralia
  3. 3.Collaborative Innovation Center of Geospatial TechnologyWuhanChina

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