Journal article

Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems

  • Zhang, Qian-Ming Web Sciences Center, School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
  • Shang, Ming-Sheng Web Sciences Center, School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
  • Zeng, Wei Web Sciences Center, School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
  • Chen, Yong Web Sciences Center, School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China -
  • Lü, Linyuan Department of Physics, University of Fribourg, Switzerland
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    13.08.2010
Published in:
  • Physics Procedia. - 2010, vol. 3, no. 5, p. 1887-1896
English Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlation between the ratings that two users give to a set of objects, such as Cosine index and Pearson correlation coefficient. However, the costs of computing this kind of indices are relatively high, and thus it is impossible to be applied in the huge-size systems. To solve this problem, in this paper, we introduce six local-structure-based similarity indices and compare their performances with the above two benchmark indices. Experimental results on two data sets demonstrate that the structure-based similarity indices overall outperform the Pearson correlation coefficient. When the data is dense, the structure-based indices can perform competitively good as Cosine index, while with lower computational complexity. Furthermore, when the data is sparse, the structure-based indices give even better results than Cosine index.
Faculty
Faculté des sciences
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301744
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