Deep Learning Expert Ranking Method Based on Listwise
LI Xian-Hui, YU Zheng-Tao, WEI Si-Chao, GAO Sheng-Xiang, WANG Li-Ren
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504 Key Laboratory of Intelligent Information Processing, Kunming University of Science and Technology, Kunming 650504
Abstract:The traditional expert list ranking method is easy to fall into local minimum, its training time is long, and the ranking function can not be approximated well. Combining listwise expert ranking with deep neural network, a deep learning expert ranking method based on listwise is proposed. Firstly, a deep learning expert ranking model is presented. Through unsupervised self-training, better parameters are obtained to initialize weights layer by layer. Then, the training instances formed by the expert documents corresponding to the queries are inputted into the restricted Boltzmann machines for the training. Finally, cosine value is used instead of matrix subtraction to compute weight. Thus, the whole replacement of weights is finished and the deep learning expert ranking model is constructed. The comparative experiments of expert ranking show that the proposed method is efficient and it improves the accuracy of ranking effectively.
[1] Balog K, Azzopardi L, de Rijke M. Formal Models for Expert Finding in Enterprise Corpora // Proc of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, USA, 2006: 43-50 [2] Kramer S, Widmer G, Pfahringer B, et al. Prediction of Ordinal Classes Using Regression Trees // Proc of the 12th International Symposium on Foundations of Intelligent Systems. Charlotte, USA, 2000: 426-434 [3] Fang Y, Si L, Mathur A P. Discriminative Models of Integrating Document Evidence and Document-Candidate Associations for Expert Search // Proc of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Geneva, Switzerland, 2010: 683-690 [4] Burges C J C, Shaked T, Renshaw E, et al. Learning to Rank Using Gradient Descent // Proc of the 22nd International Conference on Machine Learning. Bonn, Germany, 2005: 89-96 [5] Qin T, Zhang X D, Tsai M F, et al. Query-Level Loss Functions for Information Retrieval. Information Processing & Management, 2008, 44(2): 838-855 [6] Cao Z, Qin T, Liu T Y, et al. Learning to Rank: From Pairwise Approach to Listwise Approach // Proc of the 24th International Conference on Machine Learning. Corvallis, USA, 2007: 129-136 [7] Xia F, Liu T Y, Wang J, et al. Listwise Approach to Learning to Rank: Theory and Algorithm // Proc of the 25th International Conference on Machine Learning. Helsinki, Finland, 2008: 1192-1199 [8] Chen F Q, Yu Z T, Wu Z J, et al. Expert Ranking Method Based on ListNet with Multiple Features. Journal of Beijing Institute of Technology, 2014, 23(2): 240-247 [9] Collobert R, Weston J, Bottou L, et al. Natural Language Processing (almost) from Scratch. Journal of Machine Learning Research, 2011, 12: 2493-2537 [10] Glorot X, Bordes A, Bengio Y. Domain Adaptation for Large-Scale Sentiment Classication: A Deep Learning Approach // Proc of the 28th International Conference on Machine Learning. Bellevue, USA, 2011: 513-520 [11] Stuhlsatz A, Lippel J, Zielke T. Feature Extraction with Deep Neural Networks by a Generalized Discriminant Analysis. IEEE Trans on Neural Networks and Learning Systems, 2012, 23(4): 596-608 [12] Chen Y, Zheng D Q, Zhao T J. Chinese Relation Extraction Based on Deep Belief Nets. Journal of Software, 2012, 23(10): 2572-2585 (in Chinese) (陈 宇,郑德权,赵铁军.基于Deep Belief Nets的中文名实体关系抽取.软件学报, 2012, 23(10): 2572-2585) [13] Xi X F, Zhou G D. Pronoun Resolution Based on Deep Learning. Acta Scientiarum Naturalium Universitatis Pekinensis, 2014, 50(1): 100-110 (in Chinese) (奚雪峰,周国栋.基于Deep Learning的代词指代消解.北京大学学报:自然科学版, 2014, 50(1): 100-110) [14] Hinton G E, Osindero S, Teh Y W.A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 2006, 18(7): 1527-1554 [15] Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann Machines for Collaborative Filtering // Proc of the 24th International Conference on Machine Learning. Corvallis, USA, 2007: 791-798