A comparison of calibrated and intent-aware recommendations

Mesut Kaya; Derek Bridge

doi:10.1145/3298689.3347045

A comparison of calibrated and intent-aware recommendations

Mesut Kaya
, Derek Bridge

University College Cork

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

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.

Original language	English
Title of host publication	RecSys 2019 - 13th ACM Conference on Recommender Systems
Publisher	Association for Computing Machinery, Inc
Pages	151-159
Number of pages	9
ISBN (Electronic)	9781450362436
DOIs	https://doi.org/10.1145/3298689.3347045
Publication status	Published - 10 Sep 2019
Event	13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark Duration: 16 Sep 2019 → 20 Sep 2019

Publication series

Name	RecSys 2019 - 13th ACM Conference on Recommender Systems

Conference

Conference	13th ACM Conference on Recommender Systems, RecSys 2019
Country/Territory	Denmark
City	Copenhagen
Period	16/09/19 → 20/09/19

Keywords

Calibration
Diversity
Intent-aware

Access to Document

10.1145/3298689.3347045

Cite this

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Kaya, M & Bridge, D 2019, A comparison of calibrated and intent-aware recommendations. in RecSys 2019 - 13th ACM Conference on Recommender Systems. RecSys 2019 - 13th ACM Conference on Recommender Systems, Association for Computing Machinery, Inc, pp. 151-159, 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, 16/09/19. https://doi.org/10.1145/3298689.3347045

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KW - Diversity

KW - Intent-aware

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A comparison of calibrated and intent-aware recommendations

Abstract

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Conference

Keywords

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