Search Personalization Using Machine Learning
Published Online:29 Aug 2019https://doi.org/10.1287/mnsc.2018.3255
References
- (1991) Instance-based learning algorithms. Machine Learning 6(1):37–66.Crossref, Google Scholar
- (1998) On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theoretical Comput. Sci. 209(1):237–260.Crossref, Google Scholar
- (2003) E-customization. J. Marketing Res. 40(2):131–145.Crossref, Google Scholar
- (2007) Embedded premium promotion: Why it works and how to make it more effective. Marketing Sci. 26(4):514–531.Link, Google Scholar
- (2009) Learning to rank for quantity consensus queries. Allan J, Aslam J, Sanderson M, Zhai C, Zobel J, eds. Proc. 32nd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 243–250.Crossref, Google Scholar
- (2012) Modeling the impact of short- and long-term behavior on search personalization. Hersh W, Callan J, Maarek Y, Sanderson M, eds. Proc. 35th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 185–194.Crossref, Google Scholar
- (1998) Arcing classifier. Ann. Statist. 26(3):801–849.Google Scholar
- (1984) Classification and Regression Trees, The Wadsworth and Brooks-Cole Statistics-Probability Series (Springer, Cham, Switzerland).Google Scholar
- (2006) Learning to rank with nonsmooth cost functions. Adv. Neural Inform. Processing Systems (NIPS), vol. 19 (Curran Associates, Red Hook, NY), 193–200.Google Scholar
- (2005) Learning to rank using gradient descent. De Raedt L, Wrobel S, eds. Proc. 22nd Internat. Conf. Machine Learn. (ACM, New York), 89–96.Crossref, Google Scholar
- (2007) Learning to rank: From pairwise approach to listwise approach. Ghahramani Z, ed. Proc. 24th Internat. Conf. Machine Learn. (ACM, New York), 129–136.Crossref, Google Scholar
- (2006) An empirical comparison of supervised learning algorithms. Cohen W, Moore A, eds. Proc. 23rd Internat. Conf. Machine Learn. (ACM, New York), 161–168.Crossref, Google Scholar
- (2011) Yahoo! Learning to rank challenge overview. J. Machine Learn. Res. Proc. Track 14:1–24.Google Scholar
- (2016) Xgboost: A scalable tree boosting system. Aggarwal C, Smola A, Rastogi R, Shen D, Krishnapuram B, Shah M, eds. Proc. 22nd ACM Sigkdd Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 785–794.Crossref, Google Scholar
- (2008) Online learning from click data for sponsored search. Huai J, Chen R, Hon S-W, Liu Y, Ma W-Y, Tomkins S, Zhang X, eds. Proc. 17th Internat. Conf. World Wide Web (ACM, New York), 227–236.Crossref, Google Scholar
- (2013) Yandex overtakes Bing as world’s fourth search engine. Accessed February 25, 2019, http://blogs.wsj.com/tech-europe/2013/02/11/yandex-overtakes-bing-as-worlds-fourth-search-engine/.Google Scholar
- (2008) An experimental comparison of click position-bias models. Najork M, Broder A, Chakrabarti S, eds. Proc. 2008 Internat. Conf. Web Search Data Mining (ACM, New York), 87–94.Crossref, Google Scholar
- (2011) RankLib. Online. Accessed March 14, 2019, https://sourceforge.net/p/lemur/wiki/RankLib/.Google Scholar
- (2012) Testing models of consumer search using data on web browsing and purchasing behavior. Amer. Econom. Rev. 102(6):2955–2980.Crossref, Google Scholar
- (2006) Fundamentals of adaptive personalisation. Dissertation, Netherlands Research School for Information and Knowledge Systems, Utrecht, Netherlands.Google Scholar
- (2009) On the local optimality of LambdaRank. Allan J, Aslam J, Sanderson M, Zhai C, Zobel J, eds. Proc. 32nd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 460–467.Crossref, Google Scholar
- (1973) Pattern Recognition and Scene Analysis, vol. 3 (Wiley, New York).Google Scholar
- (2011) Active machine learning for consideration heuristics. Marketing Sci. 30(5):801–819.Link, Google Scholar
- (2018) Machine learning and marketing. Mizik N, Hanssens DM, eds. Handbook of Marketing Analytics (Edward Elgar Publishing, Northampton, MA), 255–279.Google Scholar
- (2013) Personalizing atypical web search sessions. Leonardi S, Panconesi A, Ferragina P, Gionis A, eds. Proc. Sixth ACM Internat. Conf. Web Search Data Mining (ACM, New York), 285–294.Crossref, Google Scholar
- (2008) A working guide to boosted regression trees. J. Animal Ecology 77(4):802–813.Crossref, Google Scholar
- (1995) Boosting a weak learning algorithm by majority. Inform. Comput. 121(2):256–285.Crossref, Google Scholar
- (1996) Experiments with a new boosting algorithm. Saitta L, ed. ICML ’96 Proc. 13th Internat. Conf. Machine Learn. (Morgan Kaufmann Publishers, San Francisco), 148–156.Google Scholar
- (2001) Greedy function approximation: A gradient boosting machine. Ann. Statist. 29(5):1189–1232.Crossref, Google Scholar
- (2011) Keywords vs. search queries: What’s the difference? Accessed February 25, 2019, http://www.wordstream.com/blog/ws/2011/05/25/keywords-vs-search-queries.Google Scholar
- (2014) Examining the impact of ranking on consumer behavior and search engine revenue. Management Sci 60(7):1632–1654.Link, Google Scholar
- (1993) Essentials of Artificial Intelligence (Morgan Kaufmann Publishers, San Francisco).Google Scholar
- (2000) Analysing web search logs to determine session boundaries for user-oriented learning. Brusilovski P, Stock O, Strapparava C, eds. Adaptive Hypermedia and Adaptive Web-Based Systems (Springer-Verlag, Berlin, Heidelberg), 319–322.Crossref, Google Scholar
- (2011) Online display advertising: Targeting and obtrusiveness. Marketing Sci. 30(3):389–404.Link, Google Scholar
- Google (2018) How search works. Accessed February 25, 2019, https://www.google.com/search/howsearchworks/crawling-indexing/.Google Scholar
- Google Official Blog (2009) Personalized search for everyone. Accessed February 25, 2019, http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone .html.Google Scholar
- (2003) An introduction to variable and feature selection. J. Machine Learn. Res. 3:1157–1182.Google Scholar
- (2013) Measuring personalization of web search. Schwabe D, Almeida V, Glaser H, Baeza-Yates R, Moon S, eds. Proc. 22nd Internat. Conf. World Wide Web (ACM, New York), 527–538.Crossref, Google Scholar
- (2003) Online ranking/collaborative filtering using the perceptron algorithm. Fawcett T, Mishra N, eds. ICML ’03 Proc. 20th Internat. Conf. Machine Learn. (AAAI Press, Palo Alto, CA), 250–257.Google Scholar
- (2009) The Elements of Statistical Learning, vol. 2 (Springer-Verlag, New York).Crossref, Google Scholar
- (2015) Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Sci 35(3):445–464.Link, Google Scholar
- (2008) Determining the informational, navigational, and transactional intent of web queries. Inform. Processing Management 44(3):1251–1266.Crossref, Google Scholar
- (2000) IR evaluation methods for retrieving highly relevant documents. Proc. 23rd Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval, SIGIR ’00 (ACM, New York), 41–48.Crossref, Google Scholar
- (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans. Inform. Systems 20(4):422–446.Crossref, Google Scholar
- Kaggle (2013) Personalized web search challenge. Accessed February 2, 2019, http://www.kaggle.com/c/yandex-personalized-web-search-challenge.Google Scholar
- Kaggle (2014) Private leaderboard—Personalized web search challenge. Accessed February 2, 2019, https://www.kaggle.com/c/yandex-personalized-web-search-challenge/leaderboard.Google Scholar
- (1997) Wrappers for feature subset selection. Artificial Intelligence 97(1):273–324.Crossref, Google Scholar
- (2014) Search for differentiated products: Identification and estimation. RAND J. Econom. 45(3):553–575.Crossref, Google Scholar
- (2013) When does retargeting work? Information specificity in online advertising. J. Marketing Res. 50(5):561–576.Crossref, Google Scholar
- (2006) Bagging and boosting classification trees to predict churn. J. Marketing Res. 43(2):276–286.Crossref, Google Scholar
- (2009) Learning to rank for information retrieval. Foundations Trends Inform. Retrieval 3(3):225–331.Crossref, Google Scholar
- (2011) Learning to Rank for Information Retrieval (Springer-Verlag, Berlin, Heidelberg).Crossref, Google Scholar
- (2014) Dataiku’s solution to Yandex’s personalized web search challenge. Technical report, Dataiku, New York.Google Scholar
- (2008) Machine learned sentence selection strategies for query-biased summarization. SIGIR Learn. Rank Workshop (ACM, New York), 40–47.Google Scholar
- (2009) Learning to disambiguate search queries from short sessions. Buntine W, Grobelnik M, Mladenić D, Shawe-Taylor J, eds. Joint Eur. Conf. Machine Learn. Knowledge Discovery Databases (Springer, Berlin, Heidelberg), 111–127.Crossref, Google Scholar
- (2012) Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, MA).Google Scholar
- (2015) Position effects in search advertising and their moderators: A regression discontinuity approach. Marketing Sci. 34(3):388–407.Link, Google Scholar
- (2006) Defection detection: Measuring and understanding the predictive accuracy of customer churn models. J. Marketing Res. 43(2):204–211.Crossref, Google Scholar
- (2012) Mine your own business: Market-structure surveillance through text-mining. Marketing Sci. 31(3):521–543.Link, Google Scholar
- (2013) Google-beater Yandex winning over Wall Street on ad view. Accessed March 14, 2019, http://www.bloomberg.com/news/2013-04-25/google-tamer-yandex-amasses-buy-ratings-russia-overnight.html.Google Scholar
- (2010) LETOR: A benchmark collection for research on learning to rank for information retrieval. Inform. Retrieval 13(4):346–374.Crossref, Google Scholar
- (2006) Automatic identification of user interest for personalized search. Carr L, De Roure D, Iyengar A, Goble C, Dahlin M, eds. Proc. 15th Internat. Conf. World Wide Web (ACM, New York), 727–736.Crossref, Google Scholar
- (1993) C4. 5: Programs for Machine Learning, vol. 1 (Morgan Kaufmann Publishing, San Francisco).Google Scholar
- (2019) Targeting and privacy in mobile advertising. Working paper, University of Washington, Seattle.Google Scholar
- (2003) Overfitting in making comparisons between variable selection methods. J. Machine Learn. Res. 3:1371–1382.Google Scholar
- (2011) Introduction to recommender systems handbook. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 1–35.Crossref, Google Scholar
- (1996) The value of purchase history data in target marketing. Marketing Sci. 15(4):321–340.Link, Google Scholar
- (1990) The strength of weak learnability. Machine Learn. 5(2):197–227.Crossref, Google Scholar
- (2012) Google: Previous query used on 0.3% of searches. Accessed February 25, 2019, http://www.seroundtable.com/google-previous-search-15924.html.Google Scholar
- ScrapeHero (2018) How many products does Amazon sell? January 2018. Accessed February 25, 2019, https://www.scrapehero.com/many-products-amazon-sell-january-2018/.Google Scholar
- (2014) Point-wise approach for Yandex personalized web search challenge. Technical report, The Institute of Electrical and Electronics Engineers, Piscataway, NJ.Google Scholar
- (1993) Cross-validating regression models in marketing research. Marketing Sci. 12(4):415–427.Link, Google Scholar
- (2011) Bing results get localized & personalized. Accessed February 25, 2019, http://searchengineland.com/bing-results-get-localized-personalized-642 84.Google Scholar
- (2008) Learning to rank answers on large online QA collections. Proc. 46th Annual Meeting Assoc. Comput. Linguistics, Columbus, Ohio, 719–727.Google Scholar
- (2008) To personalize or not to personalize: Modeling queries with variation in user intent. Chua T-S, Leong M-K, Myaeng SH, Oard DW, Sebastiani F, eds. Proc. 31st Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 163–170.Crossref, Google Scholar
- (2007) Optimization-based and machine-learning methods for conjoint analysis: Estimation and question design. Gustafsson A, Herrmann A, Huber F, eds. Conjoint Measurement: Methods and Applications (Springer-Verlag, Berlin, Heidelberg), 231.Crossref, Google Scholar
- (2017) Viewers find objectionable content on YouTube kids. Business Insider (November 7), https://www.businessinsider.com/viewers-find-objectionable-content-on-youtube-kids-2017-11.Google Scholar
- (2018) The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions. Marketing Sci. 37(4):530–552.Google Scholar
- (2014) Context models for web search personalization. Technical report, University of Toronto, Toronto.Google Scholar
- (2013) A theoretical analysis of NDCG ranking measures. PMLR 30:25–54.Google Scholar
- (1979) Optimal search for the best alternative. Econometrica 47(3):641–54.Crossref, Google Scholar
- (2010) Adapting boosting for information retrieval measures. Inform. Retrieval 13(3):254–270.Crossref, Google Scholar
- Yandex (2013) It may get really personal we have rolled out our second–generation personalised search program. Accessed February 2, 2019, http://company.yandex.com/press_center/blog/entry.xml?pid=20.Google Scholar
- Yandex (2016) Yandex announces third quarter 2016 financial results. Accessed February 2, 2019, http://ir.yandex.com/releasedetail.cfm?ReleaseID=995776.Google Scholar
- (2007) On using simultaneous perturbation stochastic approximation for IR measures, and the empirical optimality of LambdaRank. Working paper, Centre for The Initiation of Talent and Industrial Training (CITRA), Kuala Lumpur, Malaysia.Google Scholar
- (2009) The effectiveness of customized promotions in online and offline stores. J. Marketing Res. 46(2):190–206.Crossref, Google Scholar

