Utilising user texts to improve recommendations

Authors:
Yanir Seroussi
Affiliations:
Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
Venue:
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Year:
2010

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Abstract

Recommender systems traditionally rely on numeric ratings to represent user opinions, and thus are limited by the single-dimensional nature of such ratings Recent years have seen an abundance of user-generated texts available online, and advances in natural language processing allow us to better understand users by analysing the texts they write Specifically, sentiment analysis enables inference of people's sentiments and opinions from texts, while authorship attribution investigates authors' characteristics We propose to use these techniques to build text-based user models, and incorporate these models into state-of-the-art recommender systems to generate recommendations that are based on a more profound understanding of the users than rating-based recommendations Our preliminary results suggest that this is a promising direction.