One-Class Collaborative Filtering Export

@article{citeulike:5073221, abstract = {Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as one-class collaborative filtering (OCCF) problems. In these problems, the training data usually consist simply of binary data reflecting a user's action or inaction, such as page visitation in the case of news item recommendation or webpage bookmarking in the bookmarking scenario. Usually this kind of data are extremely sparse (a small fraction are positive examples), therefore ambiguity arises in the interpretation of the non-positive examples. Negative examples and unlabeled positive examples are mixed together and we are typically unable to distinguish them. For example, we cannot really attribute a user not bookmarking a page to a lack of interest or lack of awareness of the page. Previous research addressing this one-class problem only considered it as a classification task. In this paper, we consider the one-class problem under the CF setting. We propose two frameworks to tackle OCCF.}, address = {Los Alamitos, CA, USA}, author = {Pan, Rong and Zhou, Yunhong and Cao, Bin and Liu, Nathan N. and Lukose, Rajan and Scholz, Martin and Yang, Qiang}, citeulike-article-id = {5073221}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2008.16}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/ICDM.2008.16}, doi = {10.1109/ICDM.2008.16}, issn = {1550-4786}, journal = {Data Mining, IEEE International Conference on}, keywords = {2008, collaborative\_filtering, icdm, implicit\_feedback, positive\_only}, pages = {502--511}, posted-at = {2009-07-06 10:14:11}, priority = {2}, publisher = {IEEE Computer Society}, title = {One-Class Collaborative Filtering}, url = {http://dx.doi.org/10.1109/ICDM.2008.16}, volume = {0}, year = {2008} }

TY - JOUR ID - citeulike:5073221 L3 - citeulike-article-id:5073221 N2 - Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as one-class collaborative filtering (OCCF) problems. In these problems, the training data usually consist simply of binary data reflecting a user's action or inaction, such as page visitation in the case of news item recommendation or webpage bookmarking in the bookmarking scenario. Usually this kind of data are extremely sparse (a small fraction are positive examples), therefore ambiguity arises in the interpretation of the non-positive examples. Negative examples and unlabeled positive examples are mixed together and we are typically unable to distinguish them. For example, we cannot really attribute a user not bookmarking a page to a lack of interest or lack of awareness of the page. Previous research addressing this one-class problem only considered it as a classification task. In this paper, we consider the one-class problem under the CF setting. We propose two frameworks to tackle OCCF. JF - Data Mining, IEEE International Conference on SN - 1550-4786 AD - Los Alamitos, CA, USA EP - 511 TI - One-Class Collaborative Filtering PB - IEEE Computer Society VL - 0 SP - 502 KW - 2008 KW - collaborative_filtering KW - icdm KW - implicit_feedback KW - positive_only AU - Pan, Rong AU - Zhou, Yunhong AU - Cao, Bin AU - Liu, Nathan AU - Lukose, Rajan AU - Scholz, Martin AU - Yang, Qiang PY - 2008/// UR - http://dx.doi.org/10.1109/ICDM.2008.16 ER -