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226
Accurately interpreting clickthrough data as implicit feedback
- In Proceedings of SIGIR
, 2005
"... This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users ’ decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes t ..."
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Cited by 422 (7 self)
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This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users ’ decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average. Categories and Subject Descriptors
Generating query substitutions
- In WWW
, 2006
"... We introduce the notion of query substitution, that is, generating a new query to replace a user’s original search query. Our technique uses modifications based on typical substitutions web searchers make to their queries. In this way the new query is strongly related to the original query, containi ..."
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Cited by 259 (5 self)
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We introduce the notion of query substitution, that is, generating a new query to replace a user’s original search query. Our technique uses modifications based on typical substitutions web searchers make to their queries. In this way the new query is strongly related to the original query, containing terms closely related to all of the original terms. This contrasts with query expansion through pseudo-relevance feedback, which is costly and can lead to query drift. This also contrasts with query relaxation through boolean or TFIDF retrieval, which reduces the specificity of the query. We define a scale for evaluating query substitution, and show that our method performs well at generating new queries related to the original queries. We build a model for selecting between candidates, by using a number of features relating the query-candidate pair, and by fitting the model to human judgments of relevance of query suggestions. This further improves the quality of the candidates generated. Experiments show that our techniques significantly increase coverage and effectiveness in the setting of sponsored search.
Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search
- ACM TRANSACTIONS ON INFORMATION SCIENCE (TOIS
, 2007
"... This paper examines the reliability of implicit feedback generated from clickthrough data and query reformulations in WWW search. Analyzing the users ’ decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but b ..."
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Cited by 159 (19 self)
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This paper examines the reliability of implicit feedback generated from clickthrough data and query reformulations in WWW search. Analyzing the users ’ decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average. We find that such relative preferences are accurate not only between results from an individual query, but across multiple sets of results within chains of query reformulations.
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
- In Conference on Information and Knowledge Management (CIKM
, 2008
"... Most analysis of web search relevance and performance takes a single query as the unit of search engine interaction. When studies attempt to group queries together by task or session, a timeout is typically used to identify the boundary. However, users query search engines in order to accomplish tas ..."
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Cited by 133 (1 self)
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Most analysis of web search relevance and performance takes a single query as the unit of search engine interaction. When studies attempt to group queries together by task or session, a timeout is typically used to identify the boundary. However, users query search engines in order to accomplish tasks at a variety of granularities, issuing multiple queries as they attempt to accomplish tasks. In this work we study real sessions manually labeled into hierarchical tasks, and show that timeouts, whatever their length, are of limited utility in identifying task boundaries, achieving a maximum precision of only 70%. We report on properties of this search task hierarchy, as seen in a random sample of user interactions from a major web search engine’s log, annotated by human editors, learning that 17 % of tasks are interleaved, and 20 % are hierarchically organized. No previous work has analyzed or addressed automatic identification of interleaved and hierarchically organized search tasks. We propose and evaluate a method for the automated segmentation of users’ query streams into hierarchical units. Our classifiers can improve on timeout segmentation, as well as other previously published approaches, bringing the accuracy up to 92% for identifying fine-grained task boundaries, and 89-97 % for identifying pairs of queries from the same task when tasks are interleaved hierarchically. This is the first work to identify, measure and automatically segment sequences of user queries into their hierarchical structure. The ability to perform this kind of segmentation paves the way for evaluating search engines in terms of user task completion.
The Query-flow Graph: Model and Applications
, 2008
"... Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search-engine results. Mining the wealth of information available in ..."
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Cited by 111 (19 self)
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Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to search-engine results. Mining the wealth of information available in the query logs has many important applications including querylog analysis, user profiling and personalization, advertising, query recommendation, and more. In this paper we introduce the query-flow graph, a graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-flow graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same “search mission”. Any path over the query-flow graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path. The query-flow graph is an outcome of query-log mining and, at the same time, a useful tool for it. We propose a methodology that builds such a graph by mining time and textual information as well as aggregating queries from different users. Using this approach we build a real-world queryflow graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. We believe, however, that the usefulness of the query-flow graph goes beyond these two applications.
Learning diverse rankings with multi-armed bandits
- In Proceedings of the 25 th ICML
, 2008
"... Algorithms for learning to rank Web documents usually assume a document’s relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We presen ..."
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Cited by 98 (7 self)
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Algorithms for learning to rank Web documents usually assume a document’s relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users ’ clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worst-case performance even if users’ interests change. 1.
Query suggestion using hitting time
- in Proc. of conf. on Inf. and Knowledge Manage. (CIKM’08
"... Generating alternative queries, also known as query suggestion, has long been proved useful to help a user explore and express his information need. In many scenarios, such suggestions can be generated from a large scale graph of queries and other accessory information, such as the clickthrough. How ..."
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Cited by 94 (7 self)
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Generating alternative queries, also known as query suggestion, has long been proved useful to help a user explore and express his information need. In many scenarios, such suggestions can be generated from a large scale graph of queries and other accessory information, such as the clickthrough. However, how to generate suggestions while ensuring their semantic consistency with the original query remains a challenging problem. In this work, we propose a novel query suggestion algorithm based on ranking queries with the hitting time on a large scale bipartite graph. Without involvement of twisted heuristics or heavy tuning of parameters, this method clearly captures the semantic consistency between the suggested query and the original query. Empirical experiments on a large scale query log of a commercial search engine and a scientific literature collection show that hitting time is effective to generate semantically consistent query suggestions. The proposed algorithm and its variations can successfully boost long tail queries, accommodating personalized query suggestion, as well as finding related authors in research.
Active exploration for learning rankings from clickthrough data
- In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
, 2007
"... We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. In contrast, we show that an active exploration strategy can provide data that leads to much faster learning. Specificall ..."
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Cited by 77 (5 self)
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We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. In contrast, we show that an active exploration strategy can provide data that leads to much faster learning. Specifically, we develop a Bayesian approach for selecting rankings to present users so that interations result in more informative training data. Our results using the TREC-10 Web corpus, as well as synthetic data, demonstrate that a directed exploration strategy quickly leads to users being presented improved rankings in an online learning setting. We find that active exploration substantially outperforms passive observation and random exploration.
Studying the use of popular destinations to enhance Web search interaction
- ACM SIGIR '07. ACM
, 2007
"... We present a novel Web search interaction feature which, for a given query, provides links to websites frequently visited by other users with similar information needs. These popular destinations complement traditional search results, allowing direct navigation to authoritative resources for the que ..."
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Cited by 77 (14 self)
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We present a novel Web search interaction feature which, for a given query, provides links to websites frequently visited by other users with similar information needs. These popular destinations complement traditional search results, allowing direct navigation to authoritative resources for the query topic. Destinations are identified using the history of search and browsing behavior of many users over an extended time period, whose collective behavior provides a basis for computing source authority. We describe a user study which compared the suggestion of destinations with the previously proposed suggestion of related queries, as well as with traditional, unaided Web search. Results show that search enhanced by destination suggestions outperforms other systems for exploratory tasks, with best performance obtained from mining past user behavior at query-level granularity.