Providing very accurate recommendations to end users has been nowadays recognized to be just one of the main tasks a recommender systems must be able to perform. While predicting relevant suggestions, attention needs to be paid to their diversification in order to avoid monotony in recommendation. In this paper we focus on modeling users' inclination toward selecting diverse items, where diversity is computed by means of content-based item attributes. We then exploit such modeling to present a novel approach to re-rank the list of Top-N items predicted by a recommendation algorithm, in order to foster diversity in the final ranking. Experimental evaluation proves the effectiveness of the proposed approach.
|Titolo:||An analysis of users' propensity toward diversity in recommendations|
|Data di pubblicazione:||2014|
|Nome del convegno:||8th ACM Conference on Recommender systems, RecSys '14|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1145/2645710.2645774|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|