EigenRank: A ranking-oriented approach to collaborative filtering. A recommender system must be able to suggest items that are likely to be preferred by the user. In most systems, the degree of preference is represented by a rating score. Given a database of users’ past ratings on a set of items, traditional collaborative filtering algorithms are based on predicting the potential ratings that a user would assign to the unrated items so that they can be ranked by the predicted ratings to produce a list of recommended items. In this paper, we propose a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings. We measure the similarity be- tween users based on the correlation between their rankings of the items rather than the rating values and propose new collaborative filtering algorithms for ranking items based on the preferences of similar users. Experimental results on real world movie rating data sets show that the proposed approach outperforms traditional collaborative filtering al- gorithms significantly on the NDCG measure for evaluating ranked results.

References in zbMATH (referenced in 15 articles )

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  1. Shams, Bita; Haratizadeh, Saman: Reliable graph-based collaborative ranking (2018)
  2. Huang, Jiajin; Wang, Jian; Yao, Yiyu; Zhong, Ning: Cost-sensitive three-way recommendations by learning pair-wise preferences (2017)
  3. Li, Gai; Chen, Qiang: Exploiting explicit and implicit feedback for personalized ranking (2016)
  4. Shams, Bita; Haratizadeh, Saman: Sibrank: signed bipartite network analysis for neighbor-based collaborative ranking (2016)
  5. Zhao, Xiangyu; Niu, Zhendong; Wang, Kaiyi; Niu, Ke; Liu, Zhongqiang: Improving top-(N) recommendation performance using missing data (2015)
  6. Jaeger, Manfred; Lippi, Marco; Passerini, Andrea; Frasconi, Paolo: Type extension trees for feature construction and learning in relational domains (2013)
  7. Chen, Jia; Zhu, Yi-He; Wang, Hao-Fen; Jin, Wei; Yu, Yong: Effective and efficient multi-facet web image annotation (2012) ioport
  8. Farhadi, Farnoush; Sorkhi, Maryam; Hashemi, Sattar; Hamzeh, Ali: An effective framework for fast expert mining in collaboration networks: a group-oriented and cost-based method (2012) ioport
  9. Hu, Jun; Wang, Bing; Liu, Yu; Li, De-Yi: Personalized tag recommendation using social influence (2012) ioport
  10. Leroux, Philip; Dhoedt, Bart; Demeester, Piet; De Turck, Filip: Performance characterization of game recommendation algorithms on online social network sites (2012) ioport
  11. Shin, Hyoseop; Lee, Jeehoon: Impact and degree of user sociability in social media (2012) ioport
  12. Sun, Hui-Feng; Chen, Jun-Liang; Yu, Gang; Liu, Chuan-Chang; Peng, Yong; Chen, Guang; Cheng, Bo: JacUOD: A new similarity measurement for collaborative filtering (2012) ioport
  13. Treerattanapitak, Kiatichai; Jaruskulchai, Chuleerat: Exponential fuzzy C-means for collaborative filtering (2012) ioport
  14. Wu, Ying-Jun; Huang, Han; Hao, Zhi-Feng; Chen, Feng: Local community detection using link similarity (2012) ioport
  15. Xie, Hao-Ran; Li, Qing; Cai, Yi: Community-aware resource profiling for personalized search in folksonomy (2012)