research-article

A Fuzzy Recommender System for Public Library Catalogs

Authors:

Jason Morawski,

James MillerAuthors Info & Claims

International Journal of Intelligent Systems, Volume 32, Issue 10

Pages 1062 - 1084

https://doi.org/10.1002/int.21884

Published: 03 August 2017 Publication History

Abstract

Recommendation engines are one of the “discovery” products built into integrated library systems. These are a subclass of enterprise systems designed specifically for public and research libraries that incorporate an electronic card catalogue, circulation and inventory management, personnel and payroll systems, etc. The system vendors offer customizations for different contexts of specific library systems, but cannot create a bespoke solution for every customer. Our partner, an Edmonton‐area company, is filling this gap for a consortium of rural libraries in Alberta by creating a mobile app that interfaces with their electronic card catalog. Rural libraries are generally smaller than major urban public libraries, meaning that their holdings are limited overall, and within any given genre. This poses a severe problem for traditional collaborative‐filtering recommender algorithms, as the item sets for recommendations are limited by supply rather than by readers’ interests. The library's relatively small clientele also limits the item sets available for comparison. To deal with this ongoing “cold‐start” problem, we propose a hybridization of collaborative filtering with a content filter using a fuzzy taste vector. Experiments on two benchmark recommender data sets show that this approach is at least as accurate as existing fuzzy recommenders and is particularly effective on sparse data sets.

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Cited By

Ali Zaidi SFraz MShahzad MKhan S(2022)A multiapproach generalized framework for automated solution suggestion of support ticketsInternational Journal of Intelligent Systems10.1002/int.2270137:6(3654-3681)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1002/int.22701
Sardianos CVarlamis IChronis CDimitrakopoulos GAlsalemi AHimeur YBensaali FAmira A(2020)The emergence of explainability of intelligent systemsInternational Journal of Intelligent Systems10.1002/int.2231436:2(656-680)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1002/int.22314
Tabakov M(2018)Recommender System Based on Fuzzy Reasoning and Information SystemsComputational Collective Intelligence10.1007/978-3-319-98443-8_23(248-259)Online publication date: 5-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-319-98443-8_23

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Information & Contributors

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Published In

cover image International Journal of Intelligent Systems

International Journal of Intelligent Systems Volume 32, Issue 10

October 2017

124 pages

ISSN:0884-8173

DOI:10.1002/int.2017.32.issue-10

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© 2017 Wiley Periodicals, Inc.

Publisher

John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 03 August 2017

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Cited By

Ali Zaidi SFraz MShahzad MKhan S(2022)A multiapproach generalized framework for automated solution suggestion of support ticketsInternational Journal of Intelligent Systems10.1002/int.2270137:6(3654-3681)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1002/int.22701
Sardianos CVarlamis IChronis CDimitrakopoulos GAlsalemi AHimeur YBensaali FAmira A(2020)The emergence of explainability of intelligent systemsInternational Journal of Intelligent Systems10.1002/int.2231436:2(656-680)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1002/int.22314
Tabakov M(2018)Recommender System Based on Fuzzy Reasoning and Information SystemsComputational Collective Intelligence10.1007/978-3-319-98443-8_23(248-259)Online publication date: 5-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-319-98443-8_23

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