Hongtao Liu
ABSTRACT
Expert finding aims at seeking potential users to answer new questions in Community Question Answering (CQA) websites. Most existing methods focus on designing matching frameworks between questions and experts, and rely on negative sampling technology for model training. However, sampling would lose lots of useful information about experts and questions, and make these sampling-based methods suffer the bias and non-robust issues, which may lead to an insufficient matching performance for expert findings. In this paper, we propose a novel Efficient Non-sampling Expert Finding model, named ENEF, which could learn accurate representations of questions and experts from whole training data. In our approach, we adopt a rather basic question encoder and a simple matching framework, then an efficient whole-data optimization method is elaborately designed to learn the model parameters without negative sampling with rather a low space and time complexity. Extensive experimental results on four real-world CQA datasets demonstrate that our model ENEF could achieve better performance and faster training efficiency than existing state-of-the-art expert finding methods.
- Krisztian Balog, Leif Azzopardi, and Maarten de Rijke. 2009. A language modeling framework for expert finding. Information Processing & Management, Vol. 45, 1 (2009), 1--19.Google Scholar
Digital Library
- Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2020. Efficient non-sampling factorization machines for optimal context-aware recommendation. In Proceedings of The Web Conference 2020. 2400--2410.Google ScholarDigital Library
- Chong Chen, Min Zhang, Chenyang Wang, Weizhi Ma, Minming Li, Yiqun Liu, and Shaoping Ma. 2019. An efficient adaptive transfer neural network for social-aware recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 225--234.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 International Conference on Artificial Intelligence, Vol. 30.Google ScholarCross Ref
- Jinlan Fu, Yi Li, Qi Zhang, Qinzhuo Wu, Renfeng Ma, Xuanjing Huang, and Yu-Gang Jiang. 2020. Recurrent memory reasoning network for expert finding in community question answering. In Proceedings of the International Conference on Web Search and Data Mining. 187--195.Google ScholarDigital Library
- Negin Ghasemi, Ramin Fatourechi, and Saeedeh Momtazi. 2021. User Embedding for Expert Finding in Community Question Answering. Proceedings of the ACM Transactions on Knowledge Discovery from Data, Vol. 15, 4 (2021), 1--16.Google ScholarDigital Library
- Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 549--558.Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining. Ieee, 263--272.Google ScholarDigital Library
- Zongcheng Ji and Bin Wang. 2013. Learning to rank for question routing in community question answering. In Proceedings of the ACM International Conference on Information & Knowledge Management. 2363--2368.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 International Conference on Artificial Intelligence, Vol. 33. 192--199.Google ScholarDigital Library
- Xipeng Qiu and Xuanjing Huang. 2015. Convolutional neural tensor network architecture for community-based question answering. In Proceedings of the International Joint Conference on Artificial Intelligence.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the International Conference of Neural Information Processing Systems. 5998--6008.Google Scholar
- Menghan Wang, Mingming Gong, Xiaolin Zheng, and Kun Zhang. 2018. Modeling dynamic missingness of implicit feedback for recommendation. Advances in neural information processing systems, Vol. 31 (2018), 6669.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 International Conference on Web Search and Data Mining. 753--761.Google ScholarDigital Library
Index Terms
- Efficient Non-sampling Expert Finding
Recommendations
Towards a Multi-View Attentive Matching for Personalized Expert Finding
WWW '22: Proceedings of the ACM Web Conference 2022In Community Question Answering (CQA) websites, expert finding aims at seeking suitable experts to answer questions. The key is to explore the inherent relevance based on the representations of questions and experts. Existing methods usually learn these ...
ExpertBert: Pretraining Expert Finding
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementExpert Finding is an important task in Community Question Answering (CQA) platforms, which could help route questions to potential expertise users to answer. The key is to model the question content and experts based on their historical answered ...
Recurrent Memory Reasoning Network for Expert Finding in Community Question Answering
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data MiningExpert finding is a task designed to enable recommendation of the right person who can provide high-quality answers to a requester's question. Most previous works try to involve a content-based recommendation, which only superficially comprehends the ...
Comments