Proceedings ArticleDOI
Optimizing search engines using clickthrough data
Thorsten Joachims
- pp 133-142
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TLDR
The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking.Abstract:
This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.read more
Citations
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Journal ArticleDOI
A Survey on Visual Content-Based Video Indexing and Retrieval
TL;DR: Methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, and video retrieval including query interfaces are analyzed.
Proceedings ArticleDOI
Optimizing web search using social annotations
TL;DR: Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality of a webpage from the web users' perspective.
Journal ArticleDOI
Evaluating implicit measures to improve web search
TL;DR: There was an association between implicit measures of user activity and the user's explicit satisfaction ratings, and the best models for individual pages combined clickthrough, time spent on the search result page, and how a user exited a result or ended a search session.
Journal ArticleDOI
Learning to Rank for Information Retrieval
TL;DR: A statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities.
Proceedings ArticleDOI
Learning user interaction models for predicting web search result preferences
TL;DR: This work presents a real-world study of modeling the behavior of web search users to predict web search result preferences and generalizes the approach to model user behavior beyond clickthrough, which results in higher preference prediction accuracy than models based on clickthrough information alone.
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