Less is More Probabilistic Models for Retrieving Fewer Relevant Documents

By Harr Chen, David R Karger. 2006

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, neutral): “…a number of recently developed techniques for working with diverse sets, particularly in the information retrieval community [Carbonell and Goldstein, 1998, Zhai et al., 2003, Chen and Karger, 2006, Yue and Joachims, 2008, Radlinski et al., 2008, Swaminathan et al., 2009, Raman et al., 2012].”
Diversifying search results (2009, background): “[6] Harr Chen and David R. Karger.”
Towards Conversational Recommender Systems (2016, unknown): “This diversity enforcing mechanism is inspired by the approach in [6].”
Exploiting query reformulations for web search result diversification (2010, background): “ChenandKarger[8] proposed a probabilistic approach to the related problem of retrieving one relevant document for a given query.”
Online learning to diversify from implicit feedback (2012, background): “[2, 19, 3, 16, 4]), recently developed supervised learning methods for diversity have shown a lot of promise (e.”
Test Collection Based Evaluation of Information Retrieval Systems (2010, background): “However, Chen and Karger [53] showed that one can view the differences as simply two distinct, but valid, interpretations of what constitutes a relevant document.”
Essential Pages (2009, methodology): “Recently, Chen and Karger presented a method to rank documents using an optimization framework to maximize the probability of ﬁnding a relevant document in the top n [13].”
An axiomatic approach for result diversification (2009, methodology): “Subsequent work on query abandonment by Chen and Karger [8] is based on the idea that documents should be selected sequentially according to the probability of the document being relevant conditioned on the documents that come before.”
Learning diverse rankings with multi-armed bandits (2008, methodology): “Third and most importantly, except for (Chen & Karger, 2006), previous algorithms for learning to rank have considered the relevance of each document independently of other documents.”
k-DPPs: Fixed-Size Determinantal Point Processes (2011, background): “…heuristic Maximal Marginal Relevance (MMR) (Carbonell & Goldstein, 1998), diversity inducing metrics for simple probabilistic models of relevance (Chen & Karger, 2006), multi-armed bandit exploration/exploitation models (Radlinski et al., 2008), structured prediction models learned using…”
Novelty and diversity in information retrieval evaluation (2008, methodology): “From the standpoint of classic binary relevance, both the ideal gain vector and this reversed ideal gain vector are equivalent, with all relevant documents ranked .rst. 6.2 Pseudo-relevance feedback The work of Chen and Karger [13] and Amati et al. [4] suggested to us that pseudo-relevance feedba”
Graph-Based Discriminative Learning for Location Recognition (2013, background): “Instead, one can argue for diversity in such scenarios (as argued by Chen and Karger [4], among others in the information retrieval literature).”
Learning to Diversify from Implicit Feedback (2012, background): “[2, 16, 3, 14, 4]), recently developed supervised learning methods for diversity have shown a lot of promise (e.”
Designing Fair Ranking Schemes (2017, background): “Different deﬁnitions of diversity include similarity function-based [11] and topic-based [2].”
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (2012, background): “, the less-is-more effect [7]), such as friends recommendation in social networks where top-3 or top-5 performance is important.”