De-Biasing User Preference Ratings in Recommender Systems
Prior research has shown that online recommendations have significant influence on users' preference ratings and economic behavior. Specifically, the self-reported preference rating (for a specific consumed item) that is submitted by a user to a recommender system can be affected (i.e., distorted) by the previously observed system's recommendation. As a result, anchoring (or anchoring-like) biases reflected in user ratings not only provide a distorted view of user preferences but also contaminate inputs of recommender systems, leading to decreased quality of future recommendations.
Provided by: RWTH Aachen University Topic: E-Commerce Date Added: Sep 2014 Format: PDF