A method for evaluating discoverability and navigability of recommendation algorithms
Abstract Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algo...
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Online Access: | http://link.springer.com/article/10.1186/s40649-017-0045-3 |
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doaj-13d4abfce27d443da71d14531bdfefa82021-03-02T02:34:30ZengSpringerOpenComputational Social Networks2197-43142017-10-014112610.1186/s40649-017-0045-3A method for evaluating discoverability and navigability of recommendation algorithmsDaniel Lamprecht0Markus Strohmaier1Denis Helic2ISDS, Graz University of TechnologyGESIS-Leibniz Institute for the Social SciencesISDS, Graz University of TechnologyAbstract Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures proposed so far. In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the discoverability and navigability of recommendation algorithms. The proposed method tackles this by means of first evaluating the discoverability of recommendation algorithms by investigating structural properties of the resulting recommender systems in terms of bow tie structure, and path lengths. Second, the method evaluates navigability by simulating three different models of information seeking scenarios and measuring the success rates. We show the feasibility of our method by applying it to four non-personalized recommendation algorithms on three data sets and also illustrate its applicability to personalized algorithms. Our work expands the arsenal of evaluation techniques for recommendation algorithms, extends from a one-click-based evaluation towards multi-click analysis, and presents a general, comprehensive method to evaluating navigability of arbitrary recommendation algorithms.http://link.springer.com/article/10.1186/s40649-017-0045-3NavigationRecommender systemsDecentralized search |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniel Lamprecht Markus Strohmaier Denis Helic |
spellingShingle |
Daniel Lamprecht Markus Strohmaier Denis Helic A method for evaluating discoverability and navigability of recommendation algorithms Computational Social Networks Navigation Recommender systems Decentralized search |
author_facet |
Daniel Lamprecht Markus Strohmaier Denis Helic |
author_sort |
Daniel Lamprecht |
title |
A method for evaluating discoverability and navigability of recommendation algorithms |
title_short |
A method for evaluating discoverability and navigability of recommendation algorithms |
title_full |
A method for evaluating discoverability and navigability of recommendation algorithms |
title_fullStr |
A method for evaluating discoverability and navigability of recommendation algorithms |
title_full_unstemmed |
A method for evaluating discoverability and navigability of recommendation algorithms |
title_sort |
method for evaluating discoverability and navigability of recommendation algorithms |
publisher |
SpringerOpen |
series |
Computational Social Networks |
issn |
2197-4314 |
publishDate |
2017-10-01 |
description |
Abstract Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures proposed so far. In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the discoverability and navigability of recommendation algorithms. The proposed method tackles this by means of first evaluating the discoverability of recommendation algorithms by investigating structural properties of the resulting recommender systems in terms of bow tie structure, and path lengths. Second, the method evaluates navigability by simulating three different models of information seeking scenarios and measuring the success rates. We show the feasibility of our method by applying it to four non-personalized recommendation algorithms on three data sets and also illustrate its applicability to personalized algorithms. Our work expands the arsenal of evaluation techniques for recommendation algorithms, extends from a one-click-based evaluation towards multi-click analysis, and presents a general, comprehensive method to evaluating navigability of arbitrary recommendation algorithms. |
topic |
Navigation Recommender systems Decentralized search |
url |
http://link.springer.com/article/10.1186/s40649-017-0045-3 |
work_keys_str_mv |
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