2010 IEEE 10th International Conference on Data Mining

Format:

Book

Author/Creator:

IEEE International Conference on Data Mining, author.

Contributor:

IEEE Staff, Contributor.

Language:

English

Subjects (All):

Data mining--Congresses.

Data mining.

Physical Description:

1 online resource : illustrations

Place of Publication:

[Place of publication not identified] IEEE 2010

Language Note:

English

Summary:

While latent factor models are built using ratings data, which is typically assumed static, the ability to incorporate different kinds of subsequent user feedback is an important asset. For instance, the user might want to provide additional information to the system in order to improve his personal recommendations. To this end, we examine a novel scheme for efficiently learning (or refining) user parameters from such feedback. We propose a scheme where users are presented with a sequence of pair wise preference questions: "Do you prefer item A over B?". User parameters are updated based on their response, and subsequent questions are chosen adaptively after incorporating the feedback. We operate in a Bayesian framework and the choice of questions is based on an information gain criterion. We validate the scheme on the Netflix movie ratings data set. A user study and automated experiments validate our findings.

Notes:

Bibliographic Level Mode of Issuance: Monograph

ISBN:

9780769542560

0769542565