From the PRP to the Low Prior Discovery Recall Principle for Recommender Systems
Pages 1081 - 1084
Abstract
We revisit the Probability Ranking Principle in the context of recommender systems. We find a key difference in the retrieval protocol with respect to query-based search, that leads to the identification of different optimal ranking principles for discovery-oriented recommendation. Based on this finding, we revise the effectiveness of common non-personalized ranking functions in respect to the new principles. We run an experiment confirming and illustrating our theoretical analysis, and providing further observations and hints for reflection and future research.
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Index Terms
- From the PRP to the Low Prior Discovery Recall Principle for Recommender Systems
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Information & Contributors
Information
Published In
June 2018
1509 pages
ISBN:9781450356572
DOI:10.1145/3209978
- General Chairs:
- Kevyn Collins-Thompson,
- Qiaozhu Mei,
- Program Chairs:
- Brian Davison,
- Yiqun Liu,
- Emine Yilmaz
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 June 2018
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- Short-paper
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- Ministerio de Economia y Competitividad Spain
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SIGIR '18
Sponsor:
SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval
July 8 - 12, 2018
MI, Ann Arbor, USA
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SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%
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