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

Tsai, CH and Brusilovsky, P (2017) Providing control & transparency in a social recommender system for academic conferences. In: UNSPECIFIED.

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Abstract

©2017 ACM. 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.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Tsai, CHcht77@pitt.eduCHT77
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 9 July 2017
Date Type: Publication
Journal or Publication Title: UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
Page Range: 313 - 317
Event Type: Conference
DOI or Unique Handle: 10.1145/3079628.3079701
Refereed: Yes
ISBN: 9781450346351
Date Deposited: 09 Aug 2017 15:14
Last Modified: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/32809

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