On the limitations of browsing top-N recommender systems

Seyerlehner, Klaus; Flexer, Arthur; Widmer, Gerhard

doi:10.1145/1639714.1639778

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…Along the same lines, the relationship between hubness and graph theory has also been established by viewing a recommender system as a directed graph (Celma, 2010 ; Seyerlehner, Flexer, & Widmer, 2009 ). Every item in a database is a vertex of the graph and every recommendation of an item from an item is represented as a directed edge leading from to thereby constructing a nearest neighbour graph (Eppstein, Paterson, & Yao, 1997 ).…”

Section: Related Workmentioning

confidence: 99%

Mutual proximity graphs for improved reachability in music recommendation

Flexer

Stevens

2017

Journal of New Music Research

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This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists, resulting in poor reachability of the music catalogue. We present mutual proximity graphs, which are an alternative to knn and mutual knn graphs, and are able to avoid hub vertices having abnormally high connectivity. We show that mutual proximity graphs yield much better graph connectivity resulting in improved reachability compared to knn graphs, mutual knn graphs and mutual knn graphs enhanced with minimum spanning trees, while simultaneously reducing the negative effects of hubness. ARTICLE HISTORY

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Section: Related Workmentioning

confidence: 99%

Mutual proximity graphs for improved reachability in music recommendation

Flexer

Stevens

2017

Journal of New Music Research

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“…For medical documents, navigation with decentralized search was found to be comparable to human navigation (Lamprecht et al, 2015b ) and was used to point out differences in folksonomy generation algorithms (Helic et al, 2011 ). Seyerlehner et al used navigability to examine recommender systems and found top-N collaborative filtering to be inherently poorly navigable (Seyerlehner, Flexer, and Widmer, 2009 ). Lamprecht et al later confirmed this finding for the recommendation networks of the Internet Movie Database (IMDb) and suggested to use diversification to make networks more navigable (Lamprecht et al, 2015a ).…”

Section: Related Workmentioning

confidence: 99%

How the structure of Wikipedia articles influences user navigation

Lamprecht

Lerman

Helić

et al. 2016

New Review of Hypermedia and Multimedia

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In this work we study how people navigate the information network of Wikipedia and investigate (i) free-form navigation by studying all clicks within the English Wikipedia over an entire month and (ii) goal-directed Wikipedia navigation by analyzing wikigames, where users are challenged to retrieve articles by following links. To study how the organization of Wikipedia articles in terms of layout and links affects navigation behavior, we first investigate the characteristics of the structural organization and of hyperlinks in Wikipedia and then evaluate link selection models based on article structure and other potential influences in navigation, such as the generality of an article's topic. In free-form Wikipedia navigation, covering all Wikipedia usage scenarios, we find that click choices can be best modeled by a bias towards article structure, such as a tendency to click links located in the lead section. For the goal-directed navigation of wikigames, our findings confirm the zoom-out and the homing-in phases identified by previous work, where users are guided by generality at first and textual similarity to the target later. However, our interpretation of the link selection models accentuates that article structure is the best explanation for the navigation paths in all except these initial and final stages. Overall, we find evidence that users more frequently click on links that are located close to the top of an article. The structure of Wikipedia articles, which places links to more general concepts near the top, supports navigation by allowing users to quickly find the better-connected articles that facilitate navigation. Our results highlight the importance of article structure and link position in Wikipedia navigation and suggest that better organization of information can help make information networks more navigable.

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“…Responding to top‐ k recommendation's limitations in exploring the entire product space, Seyerlehner et al . proposed to recommend niche products , which were those hard‐to‐find items residing in the long tail . Kamahara proposed the community‐based partial similarity to discover unexpected items for users in the context of TV programme recommendation .…”

Section: Research Issues In Recommendation Generationmentioning

confidence: 99%

A survey of recommendation techniques based on offline data processing

Ren

Zhou

2014

Concurrency and Computation

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Summary Recommendations based on offline data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, translate the research results into real‐world applications and so on. This paper surveys the recent progress in the research of recommendations based on offline data processing, with emphasis on new techniques (such as temporal recommendation, graph‐based recommendation and trust‐based recommendation), new features (such as serendipitous recommendation) and new research issues (such as tag recommendation and group recommendation). We also provide an extensive review of evaluation measurements, benchmark data sets and available open source tools. Finally, we outline some existing challenges for future research. Copyright © 2014 John Wiley & Sons, Ltd.

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On the limitations of browsing top-N recommender systems

Cited by 13 publications

References 8 publications

Mutual proximity graphs for improved reachability in music recommendation

Mutual proximity graphs for improved reachability in music recommendation

How the structure of Wikipedia articles influences user navigation

A survey of recommendation techniques based on offline data processing

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