publication . Preprint . Other literature type . Conference object . 2018

Adversarial Sampling and Training for Semi-Supervised Information Retrieval

Dae Hoon Park; Yi Chang;
Open Access English
  • Published: 09 Nov 2018
Abstract
Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural network-based models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (...
Subjects
free text keywords: Computer Science - Information Retrieval, Computer Science - Machine Learning, Computer science, Data set, Question answering, Adversarial system, Harm, Machine learning, computer.software_genre, computer, Information retrieval, Artificial neural network, Labeled data, Artificial intelligence, business.industry, business, Sampling (statistics)
Related Organizations
42 references, page 1 of 3

[1] Alexey Borisov, Ilya Markov, Maarten de Rijke, and Pavel Serdyukov. 2016. A neural click model for web search. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 531-541.

[2] Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning. ACM, 89-96.

[3] Christopher JC Burges. 2010. From ranknet to lambdarank to lambdamart: An overview. Learning 11, 23-581 (2010), 81.

[4] Christopher J Burges, Robert Ragno, and Quoc V Le. 2007. Learning to rank with nonsmooth cost functions. In Advances in neural information processing systems. 193-200.

[5] Jingtao Ding, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li, and Depeng Jin. 2018. An improved sampler for bayesian personalized ranking by leveraging view data. In Companion of the The Web Conference 2018 on The Web Conference 2018. International World Wide Web Conferences Steering Committee, 13-14.

[6] Minwei Feng, Bing Xiang, Michael R Glass, Lidan Wang, and Bowen Zhou. 2015. Applying deep learning to answer selection: A study and an open task. arXiv preprint arXiv:1508.01585 (2015). [OpenAIRE]

[7] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics. 315-323. [OpenAIRE]

[8] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672-2680.

[9] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).

[10] Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, and Narayan Bhamidipati. 2015. Context-and content-aware embeddings for query rewriting in sponsored search. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, 383-392. [OpenAIRE]

[11] Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft. 2016. A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 55-64.

[12] F Maxwell Harper and Joseph A Konstan. 2016. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2016), 19.

[13] Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial Personalized Ranking for Recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 355-364. [OpenAIRE]

[14] Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 843-852.

[15] Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735-1780.

42 references, page 1 of 3
Related research
Abstract
Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural network-based models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (...
Subjects
free text keywords: Computer Science - Information Retrieval, Computer Science - Machine Learning, Computer science, Data set, Question answering, Adversarial system, Harm, Machine learning, computer.software_genre, computer, Information retrieval, Artificial neural network, Labeled data, Artificial intelligence, business.industry, business, Sampling (statistics)
Related Organizations
42 references, page 1 of 3

[1] Alexey Borisov, Ilya Markov, Maarten de Rijke, and Pavel Serdyukov. 2016. A neural click model for web search. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 531-541.

[2] Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning. ACM, 89-96.

[3] Christopher JC Burges. 2010. From ranknet to lambdarank to lambdamart: An overview. Learning 11, 23-581 (2010), 81.

[4] Christopher J Burges, Robert Ragno, and Quoc V Le. 2007. Learning to rank with nonsmooth cost functions. In Advances in neural information processing systems. 193-200.

[5] Jingtao Ding, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li, and Depeng Jin. 2018. An improved sampler for bayesian personalized ranking by leveraging view data. In Companion of the The Web Conference 2018 on The Web Conference 2018. International World Wide Web Conferences Steering Committee, 13-14.

[6] Minwei Feng, Bing Xiang, Michael R Glass, Lidan Wang, and Bowen Zhou. 2015. Applying deep learning to answer selection: A study and an open task. arXiv preprint arXiv:1508.01585 (2015). [OpenAIRE]

[7] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics. 315-323. [OpenAIRE]

[8] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672-2680.

[9] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).

[10] Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, and Narayan Bhamidipati. 2015. Context-and content-aware embeddings for query rewriting in sponsored search. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, 383-392. [OpenAIRE]

[11] Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft. 2016. A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 55-64.

[12] F Maxwell Harper and Joseph A Konstan. 2016. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2016), 19.

[13] Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial Personalized Ranking for Recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 355-364. [OpenAIRE]

[14] Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 843-852.

[15] Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735-1780.

42 references, page 1 of 3
Related research
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue