One-Class Collaborative Filtering

: © One-Class Collaborative Filtering Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, Qiang Yang HP Laboratories HPL-2008-133 collaborative filtering, one-class, missing values 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...

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan M. Lukose, Martin Scholz, Qiang Yang
• Keywords: Collaborative Filtering, Data Mining, ICDM 2008, One-class Collaborative Filtering, Positive Examples