%0 Conference Paper %1 Zhou:2018:EXE:3240323.3241610 %A Zhou, Qinghai %A Li, Liangyue %A Cao, Nan %A Buchler, Norbou %A Tong, Hanghang %B Proceedings of the 12th ACM Conference on Recommender Systems %C New York, NY, USA %D 2018 %I ACM %K explanation recommender %P 492--493 %R 10.1145/3240323.3241610 %T Extra: Explaining Team Recommendation in Networks %U http://doi.acm.org/10.1145/3240323.3241610 %X 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. %@ 978-1-4503-5901-6

@inproceedings{Zhou:2018:EXE:3240323.3241610, abstract = {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.}, acmid = {3241610}, added-at = {2018-12-25T18:58:46.000+0100}, address = {New York, NY, USA}, author = {Zhou, Qinghai and Li, Liangyue and Cao, Nan and Buchler, Norbou and Tong, Hanghang}, biburl = {https://www.bibsonomy.org/bibtex/22d5063ac37dd6354e15baa057bdd3323/brusilovsky}, booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems}, description = {Extra}, doi = {10.1145/3240323.3241610}, interhash = {03c8522bb3d2e23052c2b05a63a3bd11}, intrahash = {2d5063ac37dd6354e15baa057bdd3323}, isbn = {978-1-4503-5901-6}, keywords = {explanation recommender}, location = {Vancouver, British Columbia, Canada}, numpages = {2}, pages = {492--493}, publisher = {ACM}, series = {RecSys '18}, timestamp = {2019-06-09T09:32:45.000+0200}, title = {Extra: Explaining Team Recommendation in Networks}, url = {http://doi.acm.org/10.1145/3240323.3241610}, year = 2018 }