Roohollah Etemadi
ABSTRACT
The popularity of online social coding (SC) platforms such as GitHub is growing due to their social functionalities and tremendous support during the product development lifecycle. The rich information of experts' contributions on repositories can be leveraged to recruit experts for new/existing projects. In this paper, we define the problem of collaborative experts finding in SC platforms. Given a project, we model an SC platform as an attributed heterogeneous network, learn latent representations of network entities in an end-to-end manner and utilize them to discover collaborative experts to complete a project. Extensive experiments on real-world datasets from GitHub indicate the superiority of the proposed approach over the state-of-the-art in terms of a range of performance measures.
- Krisztian Balog, Leif Azzopardi, and Maarten De Rijke. 2006. Formal models for expert finding in enterprise corpora. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. 43--50. Google Scholar
Digital Library
- David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. the Journal of machine Learning research, Vol. 3 (2003), 993--1022. Google ScholarDigital Library
- Shuo Chang and AdityaPal. 2013. Routing questions for collaborative answering in community question answering. In 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013). IEEE, 494--501. Google ScholarDigital Library
- Laura Dabbish, Colleen Stuart, Jason Tsay, and Jim Herbsleb. 2012. Social coding in GitHub: transparency and collaboration in an open software repository. In Proceedings of the ACM 2012 conference on computer supported cooperative work. 1277--1286. Google ScholarDigital Library
- Zhuyun Dai, Chenyan Xiong, Jamie Callan, and Zhiyuan Liu. 2018. Convolutional neural networks for soft-matching n-grams in ad-hoc search. In Proceedings of the eleventh ACM international conference on web search and data mining.bibinfopages126--134. Google ScholarDigital Library
- Hanyin Fang, Fei Wu, Zhou Zhao, Xinyu Duan, Yueting Zhuang, and Martin Ester. 2016. Community-based question answering via heterogeneous social network learning. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. 122--128. Google ScholarDigital Library
- Jiafeng Guo, Yixing Fan, Xiang Ji, and Xueqi Cheng. 2019. MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching. In Proceedings of ACM SIGIR'19 (SIGIR'19). Google ScholarDigital Library
- Damon Horowitz and Sepandar D Kamvar. 2010. The anatomy of a large-scale social search engine. In Proceedings of the 19th international conference on World wide web. 431--440. Google ScholarDigital Library
- Kalervo Järvelin and Jaana Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, Vol. 20, 4 (2002), 422--446. Google ScholarDigital Library
- Zongcheng Ji and BinWang. 2013. Lear ning to rank for question routing in community question answering. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2363--2368. Google ScholarDigital Library
- Mehdi Kargar, Aijun An,and Morteza Zihayat. 2012. Efficient bi-objective team formation in social networks. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 483--498.Google ScholarCross Ref
- Mehdi Kargar, Lukasz Golab, Divesh Srivastava, Jaroslaw Szlichta, and Morteza Zihayat. 2020. Effective Keyword Search over Weighted Graphs. IEEE Transactions on Knowledge and Data Engineering (2020).Google ScholarDigital Library
- Abeer Khan, Lukasz Golab, Mehdi Kargar, Jaroslaw Szlichta, and Morteza Zihayat. 2020. Compact group discovery in attributed graphs and social networks. Information Processing & Management, Vol. 57, 2 (2020), 102054.Google ScholarCross Ref
- D. Kingma and J. Lei Ba. 2015. A method for stochastic optimization. International Conference on Learning Representations. San Dego: ICLR (2015).Google Scholar
- Yue Kou, Derong Shen,Quinn Snell, Dong Li, Tiezheng Nie, Ge Yu, and Shuai Ma. 2020. Efficient Team Formation in Social Networks based on Constrained Pattern Graph. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 889--900.Google ScholarCross Ref
- Theodoros Lappas, Kun Liu, and Evimaria Terzi. 2009. Finding a team of experts in social networks. In Proceedings of the 15th ACM SIGKDD. 467--476. Google ScholarDigital Library
- Baichuan Li and Irwin King. 2010. Routing questions to appropriate answerers in community question answering services. In Proceedings of the 19th ACM international conference on Information and knowledge management. 1585--1588. Google ScholarDigital Library
- Baichuan Li, Irwin King, and Michael R Lyu. 2011. Question routing in community question answering: putting category in its place. In Proceedings of the 20th ACM international conference on Information and knowledgemanagement. 2041--2044. Google ScholarDigital Library
- Zeyu Li, Jyun-Yu Jiang, Yizhou Sun, and Wei Wang. 2019. Personalized question routing via heterogeneous network embedding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 192--199.Google ScholarCross Ref
- Antonio Lima, Luca Rossi, and Mirco Musolesi. 2014. Coding together at scale: GitHub as a collaborative social network. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 8.Google Scholar
- Xiaoyong Liu, W Bruce Croft, and Matthew Koll. 2005. Finding experts in community-based question-answering services. In Proceedings of the 14th ACM international conference on Information and knowledge management. 315--316. Google ScholarDigital Library
- Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match Using Local and Distributed Representations of Text for Web Search. In Proceedings of the 26th WWW'17 (Perth, Australia)(WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1291--1299. Google ScholarDigital Library
- Jiankai Sun, Jie Zhao, Huan Sun, and Srinivasan Parthasarathy. 2020. EndCold: An End-to-End Framework for Cold Question Routing in Community Question Answering Services. In Proceedings of IJCAI'20. 3244--3250.Google ScholarCross Ref
- Andrew Turpin and Falk Scholer. 2006. User performance versus precision measures for simple search tasks. In Proceedings of ACM SIGIR'06. Google ScholarDigital Library
- https://github.com/team-formation/CoExpertsSC. 2021.Google Scholar
- https://octoverse.github.com. 2020.Google Scholar
- Yao Wan, Liang Chen, Guandong Xu, Zhou Zhao, Jie Tang, and Jian Wu. 2018. SCSMiner: mining social coding sites for software developer recommendation with relevance propagation. World Wide Web, Vol. 21, 6 (2018), 1523--1543. Google ScholarDigital Library
- Chenyan Xiong, ZhuyunDai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017. End-to-end neural ad-hoc ranking with kernel pooling. In Proceedings of ACM SIGIR'17.bibinfopages55--64. Google ScholarDigital Library
- Sha Yuan, Yu Zhang, Wendy Hall, and Juan Bautista Cabotà. 2020. Expert finding in community question answering: a review. AI Review, Vol. 53, 2 (2020), 843--874.Google Scholar
- Xuchao Zhang, Wei Cheng, Bo Zong, Yuncong Chen, Jianwu Xu, Ding Li, and Haifeng Chen. 2020. Temporal Context-Aware Representation Learning for Question Routing. In Proceedings of the 13th International Conference on Web Search and Data Mining. 753--761. Google ScholarDigital Library
- Zhou Zhao, Hanqing Lu, Vincent W Zheng, Deng Cai, Xiaofei He, and Yueting Zhuang. 2017. Community-based question answering via asymmetric multi-faceted ranking network learning. In AAAI, Vol. 17. 3532--3539. Google ScholarDigital Library
- Zhou Zhao, Qifan Yang, Deng Cai, Xiaofei He, and Yueting Zhuang. 2016. Expert finding for community-based question answering via ranking metric network learning. In International Joint Conferences on Artificial Intelligence (IJCAI), Vol. 16. 3000--3006. Google ScholarDigital Library
- Zhou Zhao, Lijun Zhang, Xiaofei He, and Wilfred Ng. 2014. Expert finding for question answering via graph regularized matrix completion. IEEE Transactions on Knowledge and Data Engineering, Vol. 27, 4 (2014), 993--1004.Google ScholarDigital Library
- Guangyou Zhou, Siwei Lai, Kang Liu, and Jun Zhao. 2012a. Topic-sensitive probabilistic model for expert finding in question answer communities. In Proceedings of the 21st ACM international conference on Information and knowledge management. 1662--1666. Google ScholarDigital Library
- Guangyou Zhou, Kang Liu, and Jun Zhao. 2012b.Joint relevance and answer quality learning for question routing in community qa. In Proceedings of the 21st ACM international conference on Information and knowledge management. 1492--1496. Google ScholarDigital Library
Index Terms
- Collaborative Experts Discovery in Social Coding Platforms
Index terms have been assigned to the content through auto-classification.
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