Rate it again: increasing recommendation accuracy by user re-rating

A common approach to designing Recommender Systems (RS) consists of asking users to explicitly rate items in order to collect feedback about their preferences. However, users have been shown to be inconsistent and to introduce a non-negligible amount of natural noise in their ratings that affects the accuracy of the predictions. In this paper, we present a novel approach to improve RS accuracy by reducing the natural noise in the input data via a preprocessing step. In order to quantitatively understand the impact of natural noise, we first analyze the response of common recommendation algorithms to this noise. Next, we propose a novel algorithm to denoise existing datasets by means of re-rating: i.e. by asking users to rate previously rated items again. This denoising step yields very significant accuracy improvements. However, re-rating all items in the original dataset is unpractical. Therefore, we study the accuracy gains obtained when re-rating only some of the ratings. In part...

• Type: Conference
• Year: 2009
• Where: RECSYS
• Authors: Xavier Amatriain, Josep M. Pujol, Nava Tintarev, Nuria Oliver
• Keywords: Control Systems, Natural Noise, RECSYS 2009, RS Accuracy, Significant Accuracy Improvements