Generating transparent, steerable recommendations from textual descriptions of items

Authors:
Stephen J. Green;Paul Lamere;Jeffrey Alexander;François Maillet;Susanna Kirk;Jessica Holt;Jackie Bourque;Xiao-Wen Mak
Affiliations:
Sun Microsystems, Burlington, MA, USA;The Echo Nest, Somerville, MA, USA;Sun Microsystems, Burlington, MA, USA;Université de Montrèal, Montrèeal, PQ, Canada;Bentley University, Waltham, MA, USA;Bentley University, Waltham, MA, USA;Bentley University, Waltham, MA, USA;Bentley University, Waltham, MA, USA
Venue:
Proceedings of the third ACM conference on Recommender systems
Year:
2009

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Abstract

We propose a recommendation technique that works by collecting text descriptions of items and using this textual aura to compute the similarity between items using techniques drawn from information retrieval. We show how this representation can be used to explain the similarities between items using terms from the textual aura and further how it can be used to steer the recommender. We describe a system that demonstrates these techniques and we'll detail some preliminary experiments aimed at evaluating the quality of the recommendations and the effectiveness of the explanations of item similarity.