ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering

Denis Kotkov; Zhao Qian; Launis Kati; Mats Neovius

doi:10.1145/3383313.3412221

ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering

Denis Kotkov, Zhao Qian, Launis Kati, Mats Neovius

Informationsteknologi

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

1 Citeringar (Scopus)

7 Nedladdningar (Pure)

Sammanfattning

Related item recommendations have a long history in recommender systems, but they tend to be a static list of similar items with respect to a target item of interest without any support of user control. In this paper, we propose ClusterExplorer, a novel approach for enabling user control over related recommendations. The approach allows users to explore the latent space of user-item interactions through controlling related recommendations. We evaluated ClusterExplorer in the book domain with 42 participants recruited in a public library and found that our approach has higher user satisfaction of browsing items and is more helpful in finding interesting items compared to traditional related item recommendations.

Originalspråk	Engelska
Titel på gästpublikation	RecSys '20: Fourteenth ACM Conference on Recommender Systems
Förlag	Association for Computing Machinery
Sidor	432–437
Antal sidor	6
ISBN (elektroniskt)	9781450375832
ISBN (tryckt)	9781450375832
DOI	https://doi.org/10.1145/3383313.3412221
Status	Publicerad - 22 sep 2020
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	conference; 2020-09-22; 2020-09-26 - Fourteenth ACM Conference on Recommender Systems Varaktighet: 22 sep 2020 → 26 sep 2020

Konferens

Konferens	conference; 2020-09-22; 2020-09-26
Period	22/09/20 → 26/09/20

Nyckelord

conversational recommender systems
critiquing recommender systems
information exploration tool
interactive recommendation
recommender systems
related item recommendations
user control
user interfaces

Åtkomst av dokument

10.1145/3383313.3412221

!ClusterExplorer Enable User Control over Related Recommendations via Collaborative Filtering and Clustering.pdfSlutlig publicerad version, 0,99 MBLicens: CC BY

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Link to publication in Scopus

1 Slutfört

LibDat: Towards a More Advanced Loaning and Reading Culture. A Study, Based on Digital Material, of Contemporary Finnish Loaning and Reading Habits
Neovius, M., Kotkov, D., Maslov, A. & Satyal, U. R.
01/09/17 → 30/08/21
Projekt: Forskning

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@inproceedings{3390358f54944bdabaee2e9f88c9dff4,

title = "ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering",

abstract = "Related item recommendations have a long history in recommender systems, but they tend to be a static list of similar items with respect to a target item of interest without any support of user control. In this paper, we propose ClusterExplorer, a novel approach for enabling user control over related recommendations. The approach allows users to explore the latent space of user-item interactions through controlling related recommendations. We evaluated ClusterExplorer in the book domain with 42 participants recruited in a public library and found that our approach has higher user satisfaction of browsing items and is more helpful in finding interesting items compared to traditional related item recommendations.",

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author = "Denis Kotkov and Zhao Qian and Launis Kati and Mats Neovius",

year = "2020",

month = sep,

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doi = "10.1145/3383313.3412221",

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booktitle = "RecSys '20: Fourteenth ACM Conference on Recommender Systems",

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ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering. / Kotkov, Denis; Qian, Zhao; Kati, Launis; Neovius, Mats.

RecSys '20: Fourteenth ACM Conference on Recommender Systems. Association for Computing Machinery, 2020. s. 432–437.

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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AB - Related item recommendations have a long history in recommender systems, but they tend to be a static list of similar items with respect to a target item of interest without any support of user control. In this paper, we propose ClusterExplorer, a novel approach for enabling user control over related recommendations. The approach allows users to explore the latent space of user-item interactions through controlling related recommendations. We evaluated ClusterExplorer in the book domain with 42 participants recruited in a public library and found that our approach has higher user satisfaction of browsing items and is more helpful in finding interesting items compared to traditional related item recommendations.

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ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering

Sammanfattning

Konferens

Nyckelord

Åtkomst av dokument

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Fingeravtryck

Projekt

LibDat: Towards a More Advanced Loaning and Reading Culture. A Study, Based on Digital Material, of Contemporary Finnish Loaning and Reading Habits

Citera det här