@article{1500,
  author = {Liang Zhang, Li Fang Peng, Phelan C.A},
  title = {Novel Recommendation of User-based Collaborative Filtering},
  journal = {Journal of Digital Information Management},
  year = {2014},
  volume = {12},
  number = {3},
  doi = {},
  url = {http://dline.info/fpaper/jdim/v12i3/1.pdf},
  abstract = {Recommendation system has been widely used in various types of e-commerce sites. One of the most successful examples is the collaborative filtering algorithm. However, the traditional algorithms only aim at accuracy and ignore these factors closely related with customer satisfaction, such as novelty etc. In this paper, we defined novelty of item from the perspective of the users, designed the corresponding offline experiment scheme and evaluation metrics. The dissimilarity and the time-popularity were embedded in the traditional collaborative filtering algorithm, the ability of predicting user's future needs and coverage of recommended list were obviously improved, and the ability of recommended long tail items were also enhanced.},
}