Hao Zhou
ABSTRACT
Advertising (ad for short) keyword suggestion is important for sponsored search to improve online advertising and increase search revenue. There are two common challenges in this task. First, the keyword bidding problem: hot ad keywords are very expensive for most of the advertisers because more advertisers are bidding on more popular keywords, while unpopular keywords are difficult to discover. As a result, most ads have few chances to be presented to the users. Second, the inefficient ad impression issue: a large proportion of search queries, which are unpopular yet relevant to many ad keywords, have no ads presented on their search result pages. Existing retrieval-based or matching-based methods either deteriorate the bidding competition or are unable to suggest novel keywords to cover more queries, which leads to inefficient ad impressions.
To address the above issues, this work investigates to use generative neural networks for keyword generation in sponsored search. Given a purchased keyword (a word sequence) as input, our model can generate a set of keywords that are not only relevant to the input but also satisfy the domain constraint which enforces that the domain category of a generated keyword is as expected. Furthermore, a reinforcement learning algorithm is proposed to adaptively utilize domain-specific information in keyword generation. Offline evaluation shows that the proposed model can generate keywords that are diverse, novel, relevant to the source keyword, and accordant with the domain constraint. Online evaluation shows that generative models can improve coverage (COV), click-through rate (CTR), and revenue per mille (RPM) substantially in sponsored search.
- Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. CoRR abs/1603.04467(2016).Google Scholar
- Vibhanshu Abhishek and Kartik Hosanagar. 2007. Keyword generation for search engine advertising using semantic similarity between terms. In Proceedings of the ninth international conference on Electronic commerce. ACM, 89-94. Google ScholarDigital Library
- Gagan Aggarwal, Ashish Goel, and Rajeev Motwani. 2006. Truthful auctions for pricing search keywords. In Proceedings of the 7th ACM conference on Electronic commerce. ACM, 1-7. Google ScholarDigital Library
- Ioannis Antonellis, Hector Garcia Molina, and Chi Chao Chang. 2008. Simrank++: query rewriting through link analysis of the click graph. Proceedings of the VLDB Endowment 1, 1 (2008), 408-421. Google ScholarDigital Library
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473(2014).Google Scholar
- Lidong Bing, Wai Lam, Tak-Lam Wong, and Shoaib Jameel. 2015. Web query reformulation via joint modeling of latent topic dependency and term context. ACM Transactions on Information Systems (TOIS) 33, 2 (2015), 6. Google ScholarDigital Library
- Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2018. Attention-based Hierarchical Neural Query Suggestion. arXiv preprint arXiv:1805.02816(2018).Google Scholar
- Yifan Chen, Gui-Rong Xue, and Yong Yu. 2008. Advertising keyword suggestion based on concept hierarchy. In Proceedings of the 2008 international conference on web search and data mining. ACM, 251-260. 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. 1724-1734.Google Scholar
- Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs/1412.3555(2014).Google Scholar
- Hang Cui, Ji-Rong Wen, Jian-Yun Nie, and Wei-Ying Ma. 2002. Probabilistic query expansion using query logs. In Proceedings of the 11th international conference on World Wide Web. ACM, 325-332. Google ScholarDigital Library
- Bruno M Fonseca, Paulo Golgher, Bruno P⊚ssas, Berthier Ribeiro-Neto, and Nivio Ziviani. 2005. Concept-based interactive query expansion. In Proceedings of the 14th ACM international conference on Information and knowledge management. ACM, 696-703. Google ScholarDigital Library
- Ariel Fuxman, Panayiotis Tsaparas, Kannan Achan, and Rakesh Agrawal. 2008. Using the wisdom of the crowds for keyword generation. In Proceedings of the 17th international conference on World Wide Web. ACM, 61-70. Google ScholarDigital Library
- Peter W Glynn. 1990. Likelihood ratio gradient estimation for stochastic systems. Commun. ACM 33, 10 (1990), 75-84. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Emil Julius Gumbel. 1954. Statistical theory of extreme values and some practical applications. NBS Applied Mathematics Series 33 (1954).Google Scholar
- Qi He, Daxin Jiang, Zhen Liao, Steven CH Hoi, Kuiyu Chang, Ee-Peng Lim, and Hang Li. 2009. Web query recommendation via sequential query prediction. In Data Engineering, 2009. ICDE'09. IEEE 25th International Conference on. IEEE, 1443-1454. Google ScholarDigital Library
- Yunlong He, Jiliang Tang, Hua Ouyang, Changsung Kang, Dawei Yin, and Yi Chang. 2016. Learning to rewrite queries. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 1443-1452. Google ScholarDigital Library
- Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144(2016).Google Scholar
- Jyun-Yu Jiang, Yen-Yu Ke, Pao-Yu Chien, and Pu-Jen Cheng. 2014. Learning user reformulation behavior for query auto-completion. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 445-454. Google ScholarDigital Library
- Thorsten Joachims. 1998. Making large-scale SVM learning practical. Technical Report. Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund.Google Scholar
- Rosie Jones, Benjamin Rey, Omid Madani, and Wiley Greiner. 2006. Generating query substitutions. In Proceedings of the 15th international conference on World Wide Web. ACM, 387-396. Google ScholarDigital Library
- Amruta Joshi and Rajeev Motwani. 2006. Keyword generation for search engine advertising. In Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. IEEE, 490-496. Google ScholarDigital Library
- Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. 2016. Improved variational inference with inverse autoregressive flow. In Advances in Neural Information Processing Systems. 4743-4751. Google ScholarDigital Library
- Kenneth Wai-Ting Leung, Wilfred Ng, and Dik Lun Lee. 2008. Personalized concept-based clustering of search engine queries. IEEE transactions on knowledge and data engineering 20, 11(2008), 1505-1518. Google ScholarDigital Library
- Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A Diversity-Promoting Objective Function for Neural Conversation Models. In NAACL. 110-119.Google Scholar
- Jiwei Li, Will Monroe, and Dan Jurafsky. 2016. A Simple, Fast Diverse Decoding Algorithm for Neural Generation. CoRR abs/1611.08562(2016).Google Scholar
- Chris J Maddison, Daniel Tarlow, and Tom Minka. 2014. A* sampling. In Advances in Neural Information Processing Systems. 3086-3094. Google ScholarDigital Library
- Robert K Merton. 1968. The Matthew effect in science: The reward and communication systems of science are considered. Science 159, 3810 (1968), 56-63.Google ScholarCross Ref
- Dandan Qiao and Jin Zhang. 2015. A Novel Keyword Suggestion Method to Achieve Competitive Advertising on Search Engines.. In PACIS. 142.Google Scholar
- Justus J Randolph. 2005. Free-Marginal Multirater Kappa (multirater K {free}): An Alternative to Fleiss' Fixed-Marginal Multirater Kappa.Online submission (2005).Google Scholar
- Sujith Ravi, Andrei Broder, Evgeniy Gabrilovich, Vanja Josifovski, Sandeep Pandey, and Bo Pang. 2010. Automatic generation of bid phrases for online advertising. In Proceedings of the third ACM international conference on Web search and data mining. ACM, 341-350. Google ScholarDigital Library
- Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C Courville, and Yoshua Bengio. 2017. A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues.. In AAAI. 3295-3301. Google ScholarDigital Library
- Lifeng Shang, Zhengdong Lu, and Hang Li. 2015. Neural Responding Machine for Short-Text Conversation. In ACL. 1577-1586.Google Scholar
- Marc Sloan, Hui Yang, and Jun Wang. 2015. A term-based methodology for query reformulation understanding. Information Retrieval Journal 18, 2 (2015), 145-165. Google ScholarDigital Library
- Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In Advances in Neural Information Processing Systems. 3483-3491. Google ScholarDigital Library
- Hyun-Je Song, A Kim, Seong-Bae Park, 2017. Translation of Natural Language Query Into Keyword Query Using a RNN Encoder-Decoder. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 965-968. 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 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 553-562. Google ScholarDigital Library
- Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In NIPS. 3104-3112. Google ScholarDigital Library
- Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning 8, 3-4 (1992), 229-256. Google ScholarDigital Library
- Shuangfei Zhai, Keng-hao Chang, Ruofei Zhang, and Zhongfei Mark Zhang. 2016. Deepintent: Learning attentions for online advertising with recurrent neural networks. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 1295-1304. Google ScholarDigital Library
- Wei Vivian Zhang and Rosie Jones. 2007. Comparing click logs and editorial labels for training query rewriting. In WWW 2007 Workshop on Query Log Analysis: Social And Technological Challenges.Google Scholar
- Ying Zhang, Weinan Zhang, Bin Gao, Xiaojie Yuan, and Tie-Yan Liu. 2014. Bid keyword suggestion in sponsored search based on competitiveness and relevance. Information Processing & Management 50, 4 (2014), 508-523.Google ScholarCross Ref
- Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017. Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 654-664.Google ScholarCross Ref
- Xianda Zhou and William Yang Wang. 2017. MojiTalk: Generating Emotional Responses at Scale. arXiv preprint arXiv:1711.04090(2017).Google Scholar
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