Al Mamunur Rashid, George Karypis, John Riedl

Recommender systems have been shown to help users and 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 recommender systems. Proposed influence measures have been algorithm-speciffic, which limits their generality and comparability. We propose an algorithm-independent definition of influence that can be applied to any ratings-based recommender system. We show experimentally that influence may be effectively estimated using simple, inexpensive metrics.

2005