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A Comparison of Calibrated and Intent-Aware Recommendations

Authors: 

Mesut Kaya, Derek Bridge

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Calibrated and intent-aware recommendation are recent approaches to recommendation that have apparent similarities. Both try, to a certain extent, to cover the user’s interests, as revealed by her user profle. In this paper, we compare them in detail. On two datasets, we show the extent to which intent-aware recommendations are calibrated and the extent to which calibrated recommendations are diverse. We consider two ways of defning a user’s interests, one based on item features, the other based on subprofles of the user’s profle. We fnd that defning interests in terms of subprofles results in highest precision and the best relevance/diversity trade-of. Along the way, we defne a new version of calibrated recommendation and three new evaluation metrics.
Conference Name: 
13th ACM Conference on Recommender Systems
Proceedings: 
Proceedings of the 13th ACM Conference on Recommender Systems
Digital Object Identifer (DOI): 
10.1145/3298689.3347045
Publication Date: 
17/09/2019
Pages: 
151-159
Conference Location: 
Denmark
Research Group: 
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
Yes
Publication document: