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A support vector method for optimizing average precision
TLDR
This work presents a general SVM learning algorithm that efficiently finds a globally optimal solution to a straightforward relaxation of MAP, and shows its method to produce statistically significant improvements in MAP scores. Expand
Learning diverse rankings with multi-armed bandits
TLDR
This work presents two online learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior and shows that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Expand
How does clickthrough data reflect retrieval quality?
TLDR
A sequence of studies investigating the relationship between observable user behavior and retrieval quality for an operational search engine on the arXiv.org e-print archive finds that paired experiment designs adapted from sensory analysis produce accurate and reliable statements about the relative quality of two retrieval functions. Expand
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
TLDR
It is found that relative preferences derived from clicks are reasonably accurate on average, and not only between results from an individual query, but across multiple sets of results within chains of query reformulations. Expand
Query chains: learning to rank from implicit feedback
TLDR
A novel approach for using clickthrough data to learn ranked retrieval functions for web search results by using query chains to generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. Expand
A Theoretical Framework for Conversational Search
TLDR
This paper studies conversational approaches to information retrieval, presenting a theory and model of information interaction in a chat setting, and shows that while theoretical, the model could be practically implemented to satisfy the desirable properties presented. Expand
Large-scale validation and analysis of interleaved search evaluation
TLDR
This paper provides a comprehensive analysis of interleaving using data from two major commercial search engines and a retrieval system for scientific literature, and analyzes the agreement ofinterleaving with manual relevance judgments and observational implicit feedback measures. Expand
TREC Complex Answer Retrieval Overview
TLDR
It is seen that combining traditional methods with learning-to-rank can outperform neural methods, even when many training queries are available, in TREC Complex Answer Retrieval. Expand
Improving personalized web search using result diversification
TLDR
Three methods to increase the diversity of the top results are proposed and evaluated and the effectiveness of these methods is evaluated. Expand
Personalizing web search using long term browsing history
TLDR
This work presents a personalization approach that builds a user interest profile using users' complete browsing behavior, then uses this model to rerank web results, and shows that using a combination of content and previously visited websites provides effective personalization. Expand
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