ABSTRACT
Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).Google Scholar
- Paul N Bennett, Filip Radlinski, Ryen W White, and Emine Yilmaz. 2011. Inferring and using location metadata to personalize web search. In Proceedings of the SIGIR'2011. ACM, 135--144. Google ScholarDigital Library
- Paul N Bennett, Krysta Svore, and Susan T Dumais. 2010. Classification-enhanced ranking. In Proceedings of the WWW'2010. ACM, 111--120. Google ScholarDigital Library
- Paul N Bennett, Ryen W White, Wei Chu, Susan T Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the impact of short-and longterm behavior on search personalization. In Proceedings of the SIGIR'2012. ACM, 185--194. Google ScholarDigital Library
- 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 ICML'2005. ACM, 89--96. Google ScholarDigital Library
- Fei Cai, Shangsong Liang, and Maarten De Rijke. 2014. Personalized document re-ranking based on bayesian probabilistic matrix factorization. In Proceedings of the SIGIR'2014. ACM, 835--838. Google ScholarDigital Library
- Mark J. Carman, Fabio Crestani, Morgan Harvey, and Mark Baillie. 2010. Towards query log based personalization using topic models. In Proceedings of the CIKM'2010. 1849--1852. Google ScholarDigital Library
- Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In EMNLP'2014. 1724--1734.Google ScholarCross Ref
- Kevyn Collins-Thompson, Paul N Bennett, Ryen W White, Sebastian De La Chica, and David Sontag. 2011. Personalizing web search results by reading level. In Proceedings of the CIKM'2011. ACM, 403--412. Google ScholarDigital Library
- Nick Craswell, Onno Zoeter, Michael J. Taylor, and Bill Ramsey. 2008. An experimental comparison of click position-bias models. In WSDM'2008. Google ScholarDigital Library
- Zhicheng Dou, Ruihua Song, and Ji-Rong Wen. 2007. A large-scale evaluation and analysis of personalized search strategies. In WWW'2007. ACM, 581--590. Google ScholarDigital Library
- Salah El Hihi and Yoshua Bengio. 1996. Hierarchical recurrent neural networks for long-term dependencies. In NIPS'1996. 493--499. Google ScholarDigital Library
- Jun Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu. 2018. Reinforcement Learning for Relation Classification From Noisy Data. In AAAI'2018.Google Scholar
- Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft. 2016. A deep relevance matching model for ad-hoc retrieval. In CIKM'2016. ACM, 55--64. Google ScholarDigital Library
- Aniko Hannak, Piotr Sapiezynski, Arash Molavi Kakhki, Balachander Krishnamurthy, David Lazer, Alan Mislove, and Christo Wilson. 2013. Measuring personalization of web search. In WWW'2013. ACM, 527--538. Google ScholarDigital Library
- Morgan Harvey, Fabio Crestani, and Mark J Carman. 2013. Building user profiles from topic models for personalised search. In CIKM'2013. ACM, 2309--2314. Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In CIKM'2013. ACM, 2333--2338. Google ScholarDigital Library
- Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In SIGKDD'2002. ACM, 133--142. Google ScholarDigital Library
- Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2005. Accurately interpreting clickthrough data as implicit feedback. In SIGIR'2005. 154--161. Google ScholarDigital Library
- Lin Li, Zhenglu Yang, Botao Wang, and Masaru Kitsuregawa. 2007. Dynamic adaptation strategies for long-term and short-term user profile to personalize search. Advances in Data and Web Management (2007), 228--240. Google ScholarDigital Library
- Xiujun Li, Chenlei Guo, Wei Chu, Ye-Yi Wang, and Jude Shavlik. 2014. Deep learning powered in-session contextual ranking using clickthrough data. In NIPS'2014.Google Scholar
- Nicolaas Matthijs and Filip Radlinski. 2011. Personalizing web search using long term browsing history. In WSDM'2011. ACM, 25--34. Google ScholarDigital Library
- Bhaskar Mitra and Nick Craswell. 2017. Neural Models for Information Retrieval. arXiv preprint arXiv:1705.01509 (2017).Google Scholar
- Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, and others. 2018. Neural information retrieval: At the end of the early years. Information Retrieval Journal 21, 2--3 (2018), 111--182. Google ScholarDigital Library
- Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward. 2016. Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. TASLP 24, 4 (2016), 694--707. Google ScholarDigital Library
- Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. In RecSys'2017. 130--137. Google ScholarDigital Library
- Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C Courville, and Joelle Pineau. 2016. Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models.. In AAAI'2016. 3776--3784. Google ScholarDigital Library
- Aliaksei Severyn and Alessandro Moschitti. 2015. Learning to rank short text pairs with convolutional deep neural networks. In SIGIR'2015. ACM, 373--382. Google ScholarDigital Library
- Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014. A latent semantic model with convolutional-pooling structure for information retrieval. In CIKM'2014. ACM, 101--110. Google ScholarDigital Library
- Ahu Sieg, Bamshad Mobasher, and Robin Burke. 2007. Web search personalization with ontological user profiles. In CIKM'2007. ACM, 525--534. Google ScholarDigital Library
- Yang Song, Hongning Wang, and Xiaodong He. 2014. Adapting deep ranknet for personalized search. In WSDM'2014. ACM, 83--92. Google ScholarDigital Library
- David Sontag, Kevyn Collins-Thompson, Paul N Bennett, Ryen W White, Susan Dumais, and Bodo Billerbeck. 2012. Probabilistic models for personalizing web search. In WSDM'2012. ACM, 433--442. Google ScholarDigital Library
- Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob Grue Simonsen, and Jian-Yun Nie. 2015. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In CIKM'2015. ACM, 553--562. Google ScholarDigital Library
- Mirco Speretta and Susan Gauch. 2005. Personalized search based on user search histories. In Web Intelligence, 2005. Proceedings. The 2005 IEEE/WIC/ACM International Conference on. IEEE, 622--628. Google ScholarDigital Library
- Jaime Teevan, Susan T Dumais, and Daniel J Liebling. 2008. To personalize or not to personalize: modeling queries with variation in user intent. In SIGIR'2018. ACM, 163--170. Google ScholarDigital Library
- Jaime Teevan, Daniel J Liebling, and Gayathri Ravichandran Geetha. 2011. Understanding and predicting personal navigation. In WSDM'2011. ACM, 85--94. Google ScholarDigital Library
- Maksims Volkovs. 2015. Context models for web search personalization. arXiv preprint arXiv:1502.00527 (2015).Google Scholar
- Thanh Vu, Dat Quoc Nguyen, Mark Johnson, Dawei Song, and Alistair Willis. 2017. Search personalization with embeddings. In ECIR'2017. Springer, 598--604.Google ScholarCross Ref
- Thanh Vu, Dawei Song, Alistair Willis, Son Ngoc Tran, and Jingfei Li. 2014. Improving search personalisation with dynamic group formation. In SIGIR'2014. 951--954. Google ScholarDigital Library
- Thanh Vu, Alistair Willis, Son N Tran, and Dawei Song. 2015. Temporal latent topic user profiles for search personalisation. In ECIR'2015. Springer, 605--616.Google ScholarCross Ref
- Hongning Wang, Xiaodong He, Ming Wei Chang, Yang Song, Ryen W. White, and Wei Chu. 2013. Personalized ranking model adaptation for web search. In SIGIR'2013. 323--332. Google ScholarDigital Library
- White, W Ryen, Bennett, N Paul, Dumais, and T Susan. 2010. Predicting shortterm interests using activity-based search context. (2010), 1009--1018. Google ScholarDigital Library
- Ryen W White, Peter Bailey, and Liwei Chen. 2009. Predicting user interests from contextual information. In SIGIR'2009. ACM, 363--370. Google ScholarDigital Library
- Ryen W White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song, and Hongning Wang. 2013. Enhancing personalized search by mining and modeling task behavior. In WWW'2013. ACM, 1411--1420. Google ScholarDigital Library
- Qiang Wu, Chris JC Burges, Krysta M Svore, and Jianfeng Gao. 2008. Ranking, boosting, and model adaptation. Technical Report. Technical report, Microsoft Research.Google Scholar
- Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017. End-to-End Neural Ad-hoc Ranking with Kernel Pooling. In SIGIR'2017. Google ScholarDigital Library
Index Terms
- Personalizing Search Results Using Hierarchical RNN with Query-aware Attention
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