@inproceedings{melville2002contentboosted,
  abstract = {Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.},
  acmid = {777124},
  address = {Menlo Park, CA, USA},
  author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass},
  booktitle = {Eighteenth National Conference on Artificial Intelligence},
  interhash = {985028099c1a29f116ad7434005895ac},
  intrahash = {a4917f0299f48e403966a8003ebd50be},
  isbn = {0-262-51129-0},
  location = {Edmonton, Alberta, Canada},
  numpages = {6},
  pages = {187--192},
  publisher = {American Association for Artificial Intelligence},
  title = {Content-boosted Collaborative Filtering for Improved Recommendations},
  url = {http://dl.acm.org/citation.cfm?id=777092.777124},
  year = 2002
}

@proceedings{jannach2014proceedings,
  bibsource = {dblp computer science bibliography, http://dblp.org},
  editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad},
  interhash = {a1a704ec9c98e6031a1444c6eccc7c0a},
  intrahash = {09cb7c63e60bd3c5e6773c9c871a8aba},
  publisher = {CEUR-WS.org},
  series = {{CEUR} Workshop Proceedings},
  title = {Proceedings of the 6th Workshop on Recommender Systems and the Social
               Web (RSWeb 2014) co-located with the 8th {ACM} Conference on Recommender
               Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014},
  url = {http://ceur-ws.org/Vol-1271},
  volume = 1271,
  year = 2014
}

@inproceedings{jannach2014sixth,
  author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  booktitle = {Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster               City, Silicon Valley, CA, {USA} - October 06 - 10, 2014},
  doi = {10.1145/2645710.2645786},
  interhash = {b465a3695da123d6ee9de1675cb3d480},
  intrahash = {5773f799bec72240eda5e6cfb6a03d7b},
  pages = 395,
  title = {The sixth {ACM} RecSys workshop on recommender systems and the social
               web},
  url = {http://doi.acm.org/10.1145/2645710.2645786},
  year = 2014
}

@misc{kang2013lalda,
  abstract = {Social media users have finite attention which limits the number of incoming
messages from friends they can process. Moreover, they pay more attention to
opinions and recommendations of some friends more than others. In this paper,
we propose LA-LDA, a latent topic model which incorporates limited,
non-uniformly divided attention in the diffusion process by which opinions and
information spread on the social network. We show that our proposed model is
able to learn more accurate user models from users' social network and item
adoption behavior than models which do not take limited attention into account.
We analyze voting on news items on the social news aggregator Digg and show
that our proposed model is better able to predict held out votes than
alternative models. Our study demonstrates that psycho-socially motivated
models have better ability to describe and predict observed behavior than
models which only consider topics.},
  author = {Kang, Jeon-Hyung and Lerman, Kristina and Getoor, Lise},
  interhash = {18a900ae003a2aedb3879fcaaa4e89b6},
  intrahash = {84ae222ddb615ca8ae9421a29c07a8f6},
  note = {cite arxiv:1301.6277Comment: The 2013 International Conference on Social Computing,  Behavioral-Cultural Modeling, & Prediction (SBP 2013)},
  title = {LA-LDA: A Limited Attention Topic Model for Social Recommendation},
  url = {http://arxiv.org/abs/1301.6277},
  year = 2013
}

@proceedings{conf/recsys/2013rsweb,
  booktitle = {RSWeb@RecSys},
  editor = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Hotho, Andreas and Anand, Sarabjot Singh and Guy, Ido},
  ee = {http://ceur-ws.org/Vol-1066},
  interhash = {31e724c09d1f4a4bbf013ecb8e1f6685},
  intrahash = {aca768068f09003e97b51d48ec092ddc},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.},
  url = {http://ceur-ws.org/Vol-1066},
  volume = 1066,
  year = 2013
}