A comparison of calibrated and intent-aware recommendations

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Date
2019-09
Authors
Kaya, Mesut
Bridge, Derek G.
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Association for Computing Machinery (ACM)
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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 profile. 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 defining a user's interests, one based on item features, the other based on subprofiles of the user's profile. We find that defining interests in terms of subprofiles results in highest precision and the best relevance/diversity trade-off. Along the way, we define a new version of calibrated recommendation and three new evaluation metrics.
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Keywords
Calibration , Intent-aware , Diversity
Citation
Kaya, M. and Bridge, D. (2019) 'A comparison of calibrated and intent-aware recommendations', RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems, Copenhagen, Denmark, 16-20 September, pp. 151-159. doi: 10.1145/3298689.3347045
Copyright
© 2019, Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems, Copenhagen, Denmark, 16-20 September, pp. 151-159. http://dx.doi.org/10.1145/3298689.3347045