Journal article

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering

  • Zeng, Wei Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China - State Key Laboratory of Networking and Switching Technology, Beijing, China - Department of Physics, University of Fribourg, Switzerland
  • Zeng, An Department of Physics, University of Fribourg, Switzerland - School of Systems Science, Beijing Normal University, China
  • Liu, Hao Department of Physics, University of Fribourg, Switzerland
  • Shang, Ming-Sheng Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China - State Key Laboratory of Networking and Switching Technology, Beijing, China
  • Zhang, Yi-Cheng Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China - Department of Physics, University of Fribourg, Switzerland - Institute of Information Economy, Hangzhou Normal University, China
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    24.10.2014
Published in:
  • PLoS ONE. - 2014, vol. 9, no. 10, p. e111005
English Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.
Faculty
Faculté des sciences
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
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Persistent URL
https://folia.unifr.ch/unifr/documents/304173
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