Machine Learning in Sponsored Search

• This repository is meant to provide some of the key papers related to machine learning for sponsored search.

• Blog post introducing basic papers

• Status on June 1st, 2017 : Added key papers for Spnosored Search and Long Tail queries.

• To be added:

1. CTR Predicition
2. Ad query relavance papers
3. Query SubGraph Papers
4. Ad quality Judgements

Please feel free to add more papers.

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Key Research Papers in Sponsored Search

1. H. Raghavan and R. Iyer. Evaluating vector-space and probabilistic models for query to ad matching. In SIGIR Workshop on Information Retrieval in Advertising, 2008.

2. D. Hillard, S. Schroedl, E. Manavoglu, H. Raghavan, and C. Leggetter. Improving ad relevance in sponsored search. In International Conference on Web Search and Data Mining, pages 361–370. ACM, 2010

3. A. Mehta. Online matching and ad allocation. Theoretical Computer Science, 8(4):265–368, 2012

4. A. Mehta, A. Saberi, U. Vazirani, and V. Vazirani. Adwords and generalized online matching. Journal of the ACM, 54(5):22, 2007.

5. T. Graepel, J. Q. Candela, T. Borchert, and R. Herbrich. Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In International Conference on Machine Learning, pages 13–20, 2010.

6. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In International Conference on World Wide Web, pages 521–530. ACM, 2007.

7. X. Li, M. Zhang, Y. Liu, S. Ma, Y. Jin, and L. Ru. Search engine click spam detection based on bipartite graph propagation. In International Conference on Web search and data mining, pages 93–102. ACM, 2014.

8. B. J. Jansen and S. Schuster. Bidding on the buying funnel for sponsored search and keyword advertising. Journal of Electronic Commerce Research, 12(1):1, 2011.

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Related Work specifically to Long Tail advertising in Sponsored Search

In the literature, the challenges of long tail queries are mainly addressed through query expansion or query reformulations. Some of the major papers in the same lines are as follows.

Query Expansion

1. A. Z. Broder, M. Fontoura, E. Gabrilovich, A. Joshi, V. Josifovski, and T. Zhang. Robust classification of rare queries using web knowledge. In International Conference on Research and Development in Information Retrieval, pages 231–238. ACM, 2007.

2. A. Broder, P. Ciccolo, E. Gabrilovich, V. Josifovski, D. Metzler, L. Riedel, and J. Yuan. Online expansion of rare queries for sponsored search. In International Conference on World Wide Web, pages 511–520. ACM, 2009.

3. A. Z. Broder, P. Ciccolo, M. Fontoura, E. Gabrilovich, V. Josifovski, and L. Riedel. Search advertising using web relevance feedback. In International Conference on Information and Knowledge Management, pages 1013–1022. ACM, 2008.

4. Y. Song and L.-w. He. Optimal rare query suggestion with implicit user feedback. In International Conference on World wide web, pages 901–910. ACM, 2010

5. Boldi, P., Bonchi, F., Castillo, C., Donato, D. and Vigna, S., 2009, February. Query suggestions using query-flow graphs. In Proceedings of the 2009 workshop on Web Search Click Data (pp. 56-63). ACM.

Query Reformulation

6. K. Zhou, X. Li, and H. Zha. Collaborative ranking: improving the relevance for tail queries. In International Conference on Information and Knowledge Management, pages 1900–1904. ACM,2012.

7. M. Verma and D. Ceccarelli. Bringing head closer to the tail with entity linking. In International Workshop on Exploiting Semantic Annotations in Information Retrieval, pages 37–39. ACM, 2014.

8. S. Chakrabarti. Dynamic personalized pagerank in entity-relation graphs. In International Conference on World Wide Web, pages 571–580. ACM, 2007.

9. I. Szpektor, A. Gionis, and Y. Maarek. Improving recommendation for long-tail queries via templates. In International Conference on World wide web, pages 47–56. ACM, 2011.

10. A. Sordoni, Y. Bengio, H. Vahabi, C. Lioma, J. Grue Simonsen, and J.-Y. Nie. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In International on Conference on Information and Knowledge Management, pages 553–562. ACM, 2015.

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