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Title: explaining team recommendation in networks
State-of-the-art in network science of teams offers effective recommendation methods to answer questions like who is the best replacement, what is the best team expansion strategy, but lacks intuitive ways to explain why the optimization algorithm gives the specific recommendation for a given team optimization scenario. To tackle this problem, we develop an interactive prototype system, Extra, as the first step towards addressing such a sense-making challenge, through the lens of the underlying network where teams embed, to explain the team recommendation results. The main advantages are (1) Algorithm efficacy: we propose an effective and fast algorithm to explain random walk graph kernel, the central technique for networked team recommendation; (2) Intuitive visual explanation: we present intuitive visual analysis of the recommendation results, which can help users better understand the rationality of the underlying team recommendation algorithm.
Authors:
; ; ; ;
Award ID(s):
1651203 1715385 1947135 2003924
Publication Date:
NSF-PAR ID:
10099213
Journal Name:
RecSys '18 Proceedings of the 12th ACM Conference on Recommender Systems
Page Range or eLocation-ID:
492 to 493
Sponsoring Org:
National Science Foundation
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