Providing control & transparency in a social recommender system for academic conferences

Chun Hua Tsai, Peter Brusilovsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

A social recommender system aims to provide useful suggestion to the user and prevent social overload problem. Most of the research efforts are spent on push high relevant item on top of the ranked list, using a weight ensemble approach. However, we argue the "learned" static fusion is not enough to specific contexts. In this paper, we develop a series visual recommendation components and control panel for the user to interact with the recommendation result of an academic conference. The system offers a better recommendation transparency and user-driven fusion through recommended sources. The experiment result shows the user did fuse the different recommended sources and exploration pa.erns among tasks. The post-study survey is positively associated with the system and explanation function effectiveness. This finding shed light on the future research of design a recommender system with human intervention and the interface beyond the static ranked list.

Original languageEnglish (US)
Title of host publicationUMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages313-317
Number of pages5
ISBN (Electronic)9781450346351
DOIs
StatePublished - Jul 9 2017
Event25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017 - Bratislava, Slovakia
Duration: Jul 9 2017Jul 12 2017

Publication series

NameUMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Conference

Conference25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017
CountrySlovakia
CityBratislava
Period7/9/177/12/17

Fingerprint

Recommender systems
Transparency
Fusion reactions
Electric fuses
Experiments

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Tsai, C. H., & Brusilovsky, P. (2017). Providing control & transparency in a social recommender system for academic conferences. In UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 313-317). (UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization). Association for Computing Machinery, Inc. https://doi.org/10.1145/3079628.3079701
Tsai, Chun Hua ; Brusilovsky, Peter. / Providing control & transparency in a social recommender system for academic conferences. UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc, 2017. pp. 313-317 (UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization).
@inproceedings{311fb8ee6b974f22b4fc9e1033ac9964,
title = "Providing control & transparency in a social recommender system for academic conferences",
abstract = "A social recommender system aims to provide useful suggestion to the user and prevent social overload problem. Most of the research efforts are spent on push high relevant item on top of the ranked list, using a weight ensemble approach. However, we argue the {"}learned{"} static fusion is not enough to specific contexts. In this paper, we develop a series visual recommendation components and control panel for the user to interact with the recommendation result of an academic conference. The system offers a better recommendation transparency and user-driven fusion through recommended sources. The experiment result shows the user did fuse the different recommended sources and exploration pa.erns among tasks. The post-study survey is positively associated with the system and explanation function effectiveness. This finding shed light on the future research of design a recommender system with human intervention and the interface beyond the static ranked list.",
author = "Tsai, {Chun Hua} and Peter Brusilovsky",
year = "2017",
month = "7",
day = "9",
doi = "10.1145/3079628.3079701",
language = "English (US)",
series = "UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization",
publisher = "Association for Computing Machinery, Inc",
pages = "313--317",
booktitle = "UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization",

}

Tsai, CH & Brusilovsky, P 2017, Providing control & transparency in a social recommender system for academic conferences. in UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, Inc, pp. 313-317, 25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017, Bratislava, Slovakia, 7/9/17. https://doi.org/10.1145/3079628.3079701

Providing control & transparency in a social recommender system for academic conferences. / Tsai, Chun Hua; Brusilovsky, Peter.

UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc, 2017. p. 313-317 (UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Providing control & transparency in a social recommender system for academic conferences

AU - Tsai, Chun Hua

AU - Brusilovsky, Peter

PY - 2017/7/9

Y1 - 2017/7/9

N2 - A social recommender system aims to provide useful suggestion to the user and prevent social overload problem. Most of the research efforts are spent on push high relevant item on top of the ranked list, using a weight ensemble approach. However, we argue the "learned" static fusion is not enough to specific contexts. In this paper, we develop a series visual recommendation components and control panel for the user to interact with the recommendation result of an academic conference. The system offers a better recommendation transparency and user-driven fusion through recommended sources. The experiment result shows the user did fuse the different recommended sources and exploration pa.erns among tasks. The post-study survey is positively associated with the system and explanation function effectiveness. This finding shed light on the future research of design a recommender system with human intervention and the interface beyond the static ranked list.

AB - A social recommender system aims to provide useful suggestion to the user and prevent social overload problem. Most of the research efforts are spent on push high relevant item on top of the ranked list, using a weight ensemble approach. However, we argue the "learned" static fusion is not enough to specific contexts. In this paper, we develop a series visual recommendation components and control panel for the user to interact with the recommendation result of an academic conference. The system offers a better recommendation transparency and user-driven fusion through recommended sources. The experiment result shows the user did fuse the different recommended sources and exploration pa.erns among tasks. The post-study survey is positively associated with the system and explanation function effectiveness. This finding shed light on the future research of design a recommender system with human intervention and the interface beyond the static ranked list.

UR - http://www.scopus.com/inward/record.url?scp=85026736003&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85026736003&partnerID=8YFLogxK

U2 - 10.1145/3079628.3079701

DO - 10.1145/3079628.3079701

M3 - Conference contribution

AN - SCOPUS:85026736003

T3 - UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

SP - 313

EP - 317

BT - UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

PB - Association for Computing Machinery, Inc

ER -

Tsai CH, Brusilovsky P. Providing control & transparency in a social recommender system for academic conferences. In UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc. 2017. p. 313-317. (UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization). https://doi.org/10.1145/3079628.3079701