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Are You Reaching Your Audience? Exploring Item Exposure over Consumer Segments in Recommender Systems


Jacek Wasilewski, Neil Hurley

Publication Type: 
Refereed Conference Meeting Proceeding
Many state-of-the-art recommender systems are known to suer from popularity bias, which means that they have a tendency to recommend items that are already popular, making those items even more popular. This results in the item catalogue being not fully utilised, which is far from ideal from the business’ perspective. Issues of item exposure are actually more complex than simply overexposure of popular items. In this paper we look at the exposure of individual items to dierent groups of consumers, the item’s audience, and address the question of whether recommender systems reach each item’s potential audience. Thus, we go beyond state-of-the-art analyses that have simply addressed the extent to which items are recommended, regardless of whether they are reaching their target audience. We conduct an empirical study on the MovieLens 20M dataset showing that recommender systems do not fully utilise items’ audiences, and existing sales diversity optimisers do not improve their exposure.
Conference Name: 
the 11th ACM Conference on Recommender Systems, Como, Italy, 27-31 August 2017
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Publication Date: 
Research Group: 
National University of Ireland, Dublin (UCD)
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