@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
}

@inproceedings{sarwar2001itembased,
  acmid = {372071},
  address = {New York, NY, USA},
  author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John},
  booktitle = {Proceedings of the 10th international conference on World Wide Web},
  doi = {10.1145/371920.372071},
  interhash = {043d1aaba0f0b8c01d84edd517abedaf},
  intrahash = {16f38785d7829500ed41c610a5eff9a2},
  isbn = {1-58113-348-0},
  location = {Hong Kong, Hong Kong},
  numpages = {11},
  pages = {285--295},
  publisher = {ACM},
  title = {Item-based collaborative filtering recommendation algorithms},
  url = {http://doi.acm.org/10.1145/371920.372071},
  year = 2001
}

@inproceedings{parra2009evaluation,
  abstract = {Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. },
  author = {Parra, Denis and Brusilovsky, Peter},
  booktitle = {Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web},
  interhash = {03a51e24ecab3ad66fcc381980144fea},
  intrahash = {42773258c36ccf2f59749991518d1784},
  issn = {1613-0073},
  location = {Torino, Italy},
  month = jun,
  series = {CEUR Workshop Proceedings},
  title = {Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike},
  url = {http://ceur-ws.org/Vol-467/paper5.pdf},
  volume = 467,
  year = 2009
}

@inproceedings{koren2009collaborative,
  abstract = {Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.},
  acmid = {1557072},
  address = {New York, NY, USA},
  author = {Koren, Yehuda},
  booktitle = {Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {10.1145/1557019.1557072},
  interhash = {ca14b78afaf26db8dd7eb13d7986830a},
  intrahash = {dad3f9050f58acf0551924e537e84e45},
  isbn = {978-1-60558-495-9},
  location = {Paris, France},
  numpages = {10},
  pages = {447--456},
  publisher = {ACM},
  title = {Collaborative filtering with temporal dynamics},
  url = {http://doi.acm.org/10.1145/1557019.1557072},
  year = 2009
}

@article{kautz1997referral,
  acmid = {245123},
  address = {New York, NY, USA},
  author = {Kautz, Henry and Selman, Bart and Shah, Mehul},
  doi = {10.1145/245108.245123},
  interhash = {6995678b936b33eef9ea1396e53a1fc7},
  intrahash = {832d16a8c86e769c7ac9ace5381f757e},
  issn = {0001-0782},
  issue_date = {March 1997},
  journal = {Communications of the ACM},
  month = mar,
  number = 3,
  numpages = {3},
  pages = {63--65},
  publisher = {ACM},
  title = {Referral Web: combining social networks and collaborative filtering},
  url = {http://doi.acm.org/10.1145/245108.245123},
  volume = 40,
  year = 1997
}