Influence in ratings-based recommender systems: An algorithm-independent approach

Al Mamunur Rashid, George Karypis, John Riedl

Research output: Contribution to conferencePaper

48 Scopus citations

Abstract

Recommender systems have been shown to help users find items of interest from among a large pool of potentially interesting items. Influence is a measure of the effect of a user on the recommendations from a recommender system. Influence is a powerful tool for understanding the workings of a recommender system. Experiments show that users have widely varying degrees of influence in ratings-based recom-mender systems. Proposed influence measures have been algorithm-specific, which limits their generality and comparability. We propose an algorithm-independent definition of influence that can be applied to any ratings-based recom-mender system. We show experimentally that influence may be effectively estimated using simple, inexpensive metrics.

Original languageEnglish (US)
Pages556-560
Number of pages5
StatePublished - Dec 1 2005
Event5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States
Duration: Apr 21 2005Apr 23 2005

Other

Other5th SIAM International Conference on Data Mining, SDM 2005
CountryUnited States
CityNewport Beach, CA
Period4/21/054/23/05

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    Rashid, A. M., Karypis, G., & Riedl, J. (2005). Influence in ratings-based recommender systems: An algorithm-independent approach. 556-560. Paper presented at 5th SIAM International Conference on Data Mining, SDM 2005, Newport Beach, CA, United States.