Author ORCID Identifier

Tsai -

Document Type

Conference Proceeding

Publication Date


Publication Title

UMAP : Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization


July 2017

First Page


Last Page



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.


© {Authors | ACM} {2017}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in {UMAP : Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization},