research-article

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Authors:

Panagiotis Adamopoulos,

Alexander TuzhilinAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 4

Article No.: 54, Pages 1 - 32

https://doi.org/10.1145/2559952

Published: 18 December 2014 Publication History

Abstract

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this article, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they would expect from the system - the consideration set of each user. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. In addition, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists. We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” datasets and compare our recommendation results with other methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 4

Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers

January 2015

390 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2699158

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 18 December 2014

Accepted: 01 November 2013

Revised: 01 August 2013

Received: 01 September 2012

Published in TIST Volume 5, Issue 4

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https://doi.org/10.3390/mti8110103
Yuan YZhou YChen XXiong QOkere H(2024)Enhancing Recommendation Diversity and Novelty with Bi-LSTM and Mean Shift ClusteringElectronics10.3390/electronics1319384113:19(3841)Online publication date: 28-Sep-2024
https://doi.org/10.3390/electronics13193841
Mohini Thakkar (2024)The Distinction Between Search and Discovery Systems : A Scholarly AnalysisInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105104910:5(622-634)Online publication date: 15-Oct-2024
https://doi.org/10.32628/CSEIT241051049
Tokutake YOkamoto K(2024)Can Large Language Models Assess Serendipity in Recommender Systems?Journal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2024.p126328:6(1263-1272)Online publication date: 20-Nov-2024
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