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Are You Reaching Your Audience? Exploring Item Exposure over Consumer Segments in Recommender Systems
Alternative Title
How Diverse Is Your Audience? Exploring Consumer Diversity in Recommender Systems
Author(s)
Date Issued
2017-08-31
Date Available
2019-04-16T10:28:05Z
Abstract
Many state-of-the-art recommender systems are known to suffer 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 different 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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2017 the Authors
Language
English
Status of Item
Peer reviewed
Conference Details
The 11th ACM Conference on Recommender Systems, Como, Italy, 27-31 August 2017
This item is made available under a Creative Commons License
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