@inproceedings{yanfei2006cubic,
  abstract = {Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.
ER  -},
  author = {Xu, Yanfei and Zhang, Liang and Liu, Wei},
  booktitle = {APWeb},
  editor = {Zhou, Xiaofang and Li, Jianzhong and Shen, Heng Tao and Kitsuregawa, Masaru and Zhang, Yanchun},
  ee = {http://dx.doi.org/10.1007/11610113_66},
  interhash = {edf999afa5a0ff81e53b0c859b466659},
  intrahash = {98dd99b5f4189c8427163fd5a7568e1d},
  isbn = {3-540-31142-4},
  journal = {Frontiers of WWW Research and Development - APWeb 2006},
  pages = {733--738},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
  url = {http://dx.doi.org/10.1007/11610113_66},
  volume = 3841,
  year = 2006
}

@article{keyhere,
  abstract = {Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER  -},
  author = {Xu, Yanfei and Zhang, Liang and Liu, Wei},
  interhash = {edf999afa5a0ff81e53b0c859b466659},
  intrahash = {5fbd24f07fe8784b516e69b0eb3192f3},
  journal = {Frontiers of WWW Research and Development - APWeb 2006},
  pages = {733--738},
  title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
  url = {http://dx.doi.org/10.1007/11610113_66},
  year = 2006
}