Efficiency Improvement of Neutrality-Enhanced Recommendation.
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma · 2013
This paper proposes an algorithm for making recommen-dations so that neutrality from a viewpoint specified by the user is enhanced. This algorithm is useful for avoid-ing decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by personalization technologies. To provide a neutrality-enhanced recommendation, we must first assume that a user can specify a particular viewpoint from which the neutrality can be applied, because a recom-mendation that is neutral from all viewpoints is no longer a recommendation. Given such a target viewpoint, we im-plement an information-neutral recommendation algorithm by introducing a penalty term to enforce statistical inde-pendence between the target viewpoint and a rating. We empirically show that our algorithm enhances the indepen-dence from the specified viewpoint.