| [12] |
Zhai C , Lafferty J . A study of smoothing methods for language models applied to Ad Hoc information retrieval. Proceedings of the 24th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval, New Orleans,USA, 2001:334～342
|
| [19] |
Carbonell J , Goldstein J . The use of mmr,diversity-based reranking for reordering documents and producing summaries. Proceedings of the 21st Annual International ACM SIGIR Conference on Research & Development on Information Retrieval, Melbourne,Australia, 1998: 335～336
|
| [20] |
Liu T Y . Learning to Rank for Information Retrieval. New York: Springer-Verlag New York Inc, 2011
|
| [21] |
Liang S S , Ren Z C , Maarten D R . Personalized search result diversification via structured learning. In Proceedings of the 20th ACM SIGKDD, New York,USA, 2014:751～760
|
| [22] |
Yue Y , Joachims T . Predicting diverse subsets using structural svms. Proceedings of the 25th ICML, Helsinki,Finland, 2008:1224～1231
|
| [23] |
Zhu Y , Lan Y , Guo J , et al. Learning for search result diversification.Proceedings of the 37th Annual International ACM SIGIR Conference on Research &Development on Information Retrieval. Proceedings of the 37th Annual International ACM SIGIR Conference on Research &Development on Information Retrieval, Gold Coast,QLD,Australia, 2014:293～302
|
| [1] |
Hofmann T . Probabilistic latent semantic analysis. Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, Stockholm,Sweden, 1999
|
| [2] |
Blei D M , Ng A Y , Jordan M I . Latent dirichlet allocation. The Journal of Machine Learning Research, 2003,3:993～1022
|
| [3] |
Metzler D , Dumais S , Meek C . Similarity measures for short segments of text. Proceedings of the 29th European Conference on IR Research, Rome,Italy, 2007:16～27
|
| [4] |
Hong L , Davison B . Empirical study of topic modeling in Twitter. Proceedings of the 1st Workshop on Social Media Analytics, Washington DC,USA, 2010: 80～88
|
| [5] |
Zhao W , Jiang J , Weng J , et al. Comparing Twitter and traditional media using topic models. Proceedings of the 33rd European Conference on IR Research, Dublin,Ireland, 2011:338～349
|
| [6] |
Lakkaraju H , Bhattacharya I , Bhattacharyya C , et al. Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media. Proceedings of the 12th IEEE International Conference on Data Mining, Brussels,Belgium, 2012
|
| [7] |
Yan X H , Guo J F , Lan Y Y , et al. A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web,Rio de Janeiro, Brazil, 2013:1445～1456
|
| [8] |
Cheng X Q , Yan X H , Lan Y Y , et al. BTM: topic modeling over short texts. IEEE Transactions on Knowledge and Data Engineering, 2014,26(12):2928～2941
|
| [9] |
Yan X H , Guo J F , Lan Y Y , et al. A probabilistic model for bursty topic discovery in microblogs. Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin Texas,USA, 2015
|
| [10] |
Bengio Y , Ducharme R , Vincent P , et al. A neural probabilistic language model. Journal of Machine Learning Research, 2003,3:1137～1155
|
| [11] |
Morin F , Bengio Y . Hierarchical probabilistic neural network language model. Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, Barbados, 2005
|
| [12] |
Mnih A , Hinton G . Three new graphical models for statistical language modelling. Proceedings of the 24th International Conference on Machine Learning, New York,USA, 2007: 641～648
|
| [13] |
Mnih A , Hinton G E . A scalable hierarchical distributed language model. Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver,Canada, 2009
|
| [14] |
Mnih A , Kavukcuoglu K . Learning word embeddings efficiently with noise-contrastive estimation. Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe,Nevada,USA, 2013
|
| [15] |
Mikolov T , Chen K , Corrado G , et al. Efficient estimation of word representations in vector space. Proceedings of Workshop of ICLR, Arizona,USA, 2013
|
| [16] |
Sun F , Guo J F , Lan Y Y , et al. Learning word representation by jointly modeling syntagmatic and paradigmatic relations. Proceedings of the 53rd Annual Metting of the Association for Computational Linguistics, Beijing,China, 2015
|
| [24] |
Xia L , Xu J , Lan Y Y , et al. Learning maximal marginal relevance model via directly optimizing diversity evaluation measures.. Proceedings of the 38th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago,Chile, 2015
|
| [17] |
Robertson S E . Overview of the okapi projects. Journal of Documentation, 1997,53(1): 3～7
|
