NDLI: Providing Control and Transparency in a Social Recommender System for Academic Conferences

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Providing Control and Transparency in a Social Recommender System for Academic Conferences

Content Provider	ACM Digital Library
Author	Tsai, Chun-Hua Brusilovsky, Peter
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 patterns 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.
Starting Page	313
Ending Page	317
Page Count	5
File Format	PDF
ISBN	9781450346351
DOI	10.1145/3079628.3079701
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-07-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User control Transparency Explanation Social recommendation
Content Type	Text
Resource Type	Article

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