Popularity tendency analysis of ranking-oriented collaborative filtering from the perspective of loss function

Xudong MAO, Qing LI, Haoran XIE, Yanghui RAO

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

Collaborative filtering (CF) has been the most popular approach for recommender systems in recent years. In order to analyze the property of a ranking-oriented CF algorithm directly and be able to improve its performance, this paper investigates the ranking-oriented CF from the perspective of loss function. To gain the insight into the popular bias problem, we also study the tendency of a CF algorithm in recommending the most popular items, and show that such popularity tendency can be adjusted through setting different parameters in our models. After analyzing two state-of-the-art algorithms, we propose in this paper two models using the generalized logistic loss function and the hinge loss function, respectively. The experimental results show that the proposed methods outperform the state-of-the-art algorithms on two real data sets. Copyright © 2014 Springer International Publishing Switzerland.

Original languageEnglish
Title of host publicationDatabase systems for advanced applications: 19th International Conference, DASFAA 2014, Bali, Indonesia, April 21-24, 2014, proceedings
EditorsSourav S. BHOWMICK, Curtis E. DYRESON, Christian S. JENSEN, Mong Li LEE, Agus MULIANTARA, Bernhard THALHEIM
PublisherSpringer International Publishing
Pages451-465
VolumePart I
ISBN (Electronic)9783319058108
ISBN (Print)9783319058092
DOIs
Publication statusPublished - 2014

Fingerprint

Collaborative filtering
Recommender systems
Hinges
Logistics

Citation

Mao, X., Li, Q., Xie, H., & Rao, Y. (2014). Popularity tendency analysis of ranking-oriented collaborative filtering from the perspective of loss function. In S. S. Bhowmick, C. E. Dyreson, C. S. Jensen, M. L. Lee, A. Muliantara, & B. Thalheim (Eds.), Database systems for advanced applications: 19th International Conference, DASFAA 2014, Bali, Indonesia, April 21-24, 2014, proceedings (Pt. 1, pp. 451-465). Cham: Springer.

Keywords

  • Collaborative filtering
  • Matrix factorization
  • Loss function