2012 OnlineLearningtoDiversifyfromIm

• (Raman et al., 2012) ⇒ Karthik Raman, Pannaga Shivaswamy, and Thorsten Joachims. (2012). “Online Learning to Diversify from Implicit Feedback.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339642

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Online+Learning+to+Diversify+from+Implicit+Feedback
• http://dl.acm.org/citation.cfm?id=2339530.2339642&preflayout=flat#citedby

Quotes

Author Keywords

• Diversified retrieval; learning; online learning; retrieval models; submodularity

Abstract

In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these diversification mechanisms are largely hand-coded or relied on expensive training data provided by experts. To overcome this problem, we propose an online learning model and algorithms for learning diversified recommendations and retrieval functions from implicit feedback. In our model, the learning algorithm presents a ranking to the user at each step, and uses the set of documents from the presented ranking, which the user reads, as feedback. Even for imperfect and noisy feedback, we show that the algorithms admit theoretical guarantees for maximizing any submodular utility measure under approximately rational user behavior. In addition to the theoretical results, we find that the algorithm learns quickly, accurately, and robustly in empirical evaluations on two datasets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 OnlineLearningtoDiversifyfromIm	Thorsten Joachims Karthik Raman Pannaga Shivaswamy			Online Learning to Diversify from Implicit Feedback				10.1145/2339530.2339642		2012