The Experts below are selected from a list of 3021 Experts worldwide ranked by ideXlab platform
Amanda Spink - One of the best experts on this subject based on the ideXlab platform.
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Analyzing web multimedia Query Reformulation behavior
2009Co-Authors: Liang-chun Jack Tseng, Dian Tjondronegoro, Amanda SpinkAbstract:Current multimedia Web search engines still use keywords as the primary means to search. Due to the richness in multimedia contents, general users constantly experience some difficulties in formulating textual queries that are representative enough for their needs. As a result, Query Reformulation becomes part of an inevitable process in most multimedia searches. Previous Web Query formulation studies did not investigate the modification sequences and thus can only report limited findings on the Reformulation behavior. In this study, we propose an automatic approach to examine multimedia Query Reformulation using large-scale transaction logs. The key findings show that search term replacement is the most dominant type of modifications in visual searches but less important in audio searches. Image search users prefer the specified search strategy more than video and audio users. There is also a clear tendency to replace terms with synonyms or associated terms in visual queries. The analysis of the search strategies in different types of multimedia searching provides some insights into user’s searching behavior, which can contribute to the design of future Query formulation assistance for keyword-based Web multimedia retrieval systems.
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patterns of Query Reformulation during web searching
Journal of the Association for Information Science and Technology, 2009Co-Authors: Bernard J. Jansen, Danielle L. Booth, Amanda SpinkAbstract:Query Reformulation is a key user behavior during Web search. Our research goal is to develop predictive models of Query Reformulation during Web searching. This article reports results from a study in which we automatically classified the Query-Reformulation patterns for 964,780 Web searching sessions, composed of 1,523,072 queries, to predict the next Query Reformulation. We employed an n-gram modeling approach to describe the probability of users transitioning from one Query-Reformulation state to another to predict their next state. We developed first-, second-, third-, and fourth-order models and evaluated each model for accuracy of prediction, coverage of the dataset, and complexity of the possible pattern set. The results show that Reformulation and Assistance account for approximately 45p of all Query Reformulations; furthermore, the results demonstrate that the first- and second-order models provide the best predictability, between 28 and 40p overall and higher than 70p for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance. © 2009 Wiley Periodicals, Inc.
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predicting Query Reformulation during web searching
Human Factors in Computing Systems, 2009Co-Authors: Bernard J. Jansen, Danielle L. Booth, Amanda SpinkAbstract:his paper reports results from a study in which we automatically classified the Query Reformulation patterns for 964,780 Web searching sessions (composed of 1,523,072 queries) in order to predict what the next Query Reformulation would be. We employed an n-gram modeling approach to describe the probability of searchers transitioning from one Query Reformulation state to another and predict their next state. We developed first, second, third, and fourth order models and evaluated each model for accuracy of prediction. Findings show that Reformulation and Assistance account for approximately 45 percent of all Query Reformulations. Searchers seem to seek system searching assistant early in the session or after a content change. The results of our evaluations show that the first and second order models provided the best predictability, between 28 and 40 percent overall, and higher than 70 percent for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance in real time.
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Patterns of Query Reformulation during Web searching
Journal of the American Society for Information Science and Technology, 2009Co-Authors: Bernard J. Jansen, Danielle L. Booth, Amanda SpinkAbstract:Query Reformulation is a key user behavior during Web search. Our research goal is to develop predictive models of Query Reformulation during Web searching. This article reports results from a study in which we automatically classified the Query-Reformulation patterns for 964,780 Web searching sessions, composed of 1,523,072 queries, to predict the next Query Reformulation. We employed an n-gram modeling approach to describe the probability of users transitioning from one Query-Reformulation state to another to predict their next state. We developed first-, second-, third-, and fourth-order models and evaluated each model for accuracy of prediction, coverage of the dataset, and complexity of the possible pattern set. The results show that Reformulation and Assistance account for approximately 45% of all Query Reformulations; furthermore, the results demonstrate that the first- and second-order models provide the best predictability, between 28 and 40% overall and higher than 70% for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance
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CHI Extended Abstracts - Predicting Query Reformulation during web searching
Proceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA '09, 2009Co-Authors: Bernard J. Jansen, Danielle L. Booth, Amanda SpinkAbstract:his paper reports results from a study in which we automatically classified the Query Reformulation patterns for 964,780 Web searching sessions (composed of 1,523,072 queries) in order to predict what the next Query Reformulation would be. We employed an n-gram modeling approach to describe the probability of searchers transitioning from one Query Reformulation state to another and predict their next state. We developed first, second, third, and fourth order models and evaluated each model for accuracy of prediction. Findings show that Reformulation and Assistance account for approximately 45 percent of all Query Reformulations. Searchers seem to seek system searching assistant early in the session or after a content change. The results of our evaluations show that the first and second order models provided the best predictability, between 28 and 40 percent overall, and higher than 70 percent for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance in real time.
Simon Dennis - One of the best experts on this subject based on the ideXlab platform.
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interactive internet search keyword directory and Query Reformulation mechanisms compared
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2000Co-Authors: Peter Bruza, Robert Mcarthur, Simon DennisAbstract:This article compares search effectiveness when using Query-based Internet search (via the Google search engine), directory-based search (via Yahoo) and phrase-based Query Reformulation assisted search (via the Hyperindex browser) by means of a controlled, user-based experimental study. The focus was to evaluate aspects of the search process. Cognitive load was measured using a secondary digit-monitoring task to quantify the effort of the user in various search states; independent relevance judgements were employed to gauge the quality of the documents accessed during the search process. Time was monitored in various search states. Results indicated the directory-based search does not offer increased relevance over the Query-based search (with or without Query formulation assistance), and also takes longer. Query Reformulation does significantly improve the relevance of the documents through which the user must trawl versus standard Query-based internet search. However, the improvement in document relevance comes at the cost of increased search time and increased cognitive load.
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SIGIR - Interactive Internet search: keyword, directory and Query Reformulation mechanisms compared
Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '00, 2000Co-Authors: Peter Bruza, Robert Mcarthur, Simon DennisAbstract:This article compares search effectiveness when using Query-based Internet search (via the Google search engine), directory-based search (via Yahoo) and phrase-based Query Reformulation assisted search (via the Hyperindex browser) by means of a controlled, user-based experimental study. The focus was to evaluate aspects of the search process. Cognitive load was measured using a secondary digit-monitoring task to quantify the effort of the user in various search states; independent relevance judgements were employed to gauge the quality of the documents accessed during the search process. Time was monitored in various search states. Results indicated the directory-based search does not offer increased relevance over the Query-based search (with or without Query formulation assistance), and also takes longer. Query Reformulation does significantly improve the relevance of the documents through which the user must trawl versus standard Query-based internet search. However, the improvement in document relevance comes at the cost of increased search time and increased cognitive load.
Bernard J. Jansen - One of the best experts on this subject based on the ideXlab platform.
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patterns of Query Reformulation during web searching
Journal of the Association for Information Science and Technology, 2009Co-Authors: Bernard J. Jansen, Danielle L. Booth, Amanda SpinkAbstract:Query Reformulation is a key user behavior during Web search. Our research goal is to develop predictive models of Query Reformulation during Web searching. This article reports results from a study in which we automatically classified the Query-Reformulation patterns for 964,780 Web searching sessions, composed of 1,523,072 queries, to predict the next Query Reformulation. We employed an n-gram modeling approach to describe the probability of users transitioning from one Query-Reformulation state to another to predict their next state. We developed first-, second-, third-, and fourth-order models and evaluated each model for accuracy of prediction, coverage of the dataset, and complexity of the possible pattern set. The results show that Reformulation and Assistance account for approximately 45p of all Query Reformulations; furthermore, the results demonstrate that the first- and second-order models provide the best predictability, between 28 and 40p overall and higher than 70p for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance. © 2009 Wiley Periodicals, Inc.
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predicting Query Reformulation during web searching
Human Factors in Computing Systems, 2009Co-Authors: Bernard J. Jansen, Danielle L. Booth, Amanda SpinkAbstract:his paper reports results from a study in which we automatically classified the Query Reformulation patterns for 964,780 Web searching sessions (composed of 1,523,072 queries) in order to predict what the next Query Reformulation would be. We employed an n-gram modeling approach to describe the probability of searchers transitioning from one Query Reformulation state to another and predict their next state. We developed first, second, third, and fourth order models and evaluated each model for accuracy of prediction. Findings show that Reformulation and Assistance account for approximately 45 percent of all Query Reformulations. Searchers seem to seek system searching assistant early in the session or after a content change. The results of our evaluations show that the first and second order models provided the best predictability, between 28 and 40 percent overall, and higher than 70 percent for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance in real time.
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Patterns of Query Reformulation during Web searching
Journal of the American Society for Information Science and Technology, 2009Co-Authors: Bernard J. Jansen, Danielle L. Booth, Amanda SpinkAbstract:Query Reformulation is a key user behavior during Web search. Our research goal is to develop predictive models of Query Reformulation during Web searching. This article reports results from a study in which we automatically classified the Query-Reformulation patterns for 964,780 Web searching sessions, composed of 1,523,072 queries, to predict the next Query Reformulation. We employed an n-gram modeling approach to describe the probability of users transitioning from one Query-Reformulation state to another to predict their next state. We developed first-, second-, third-, and fourth-order models and evaluated each model for accuracy of prediction, coverage of the dataset, and complexity of the possible pattern set. The results show that Reformulation and Assistance account for approximately 45% of all Query Reformulations; furthermore, the results demonstrate that the first- and second-order models provide the best predictability, between 28 and 40% overall and higher than 70% for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance
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CHI Extended Abstracts - Predicting Query Reformulation during web searching
Proceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA '09, 2009Co-Authors: Bernard J. Jansen, Danielle L. Booth, Amanda SpinkAbstract:his paper reports results from a study in which we automatically classified the Query Reformulation patterns for 964,780 Web searching sessions (composed of 1,523,072 queries) in order to predict what the next Query Reformulation would be. We employed an n-gram modeling approach to describe the probability of searchers transitioning from one Query Reformulation state to another and predict their next state. We developed first, second, third, and fourth order models and evaluated each model for accuracy of prediction. Findings show that Reformulation and Assistance account for approximately 45 percent of all Query Reformulations. Searchers seem to seek system searching assistant early in the session or after a content change. The results of our evaluations show that the first and second order models provided the best predictability, between 28 and 40 percent overall, and higher than 70 percent for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance in real time.
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Patterns and transitions of Query Reformulation during web searching
International Journal of Web Information Systems, 2007Co-Authors: Bernard J. Jansen, Mimi Zhang, Amanda SpinkAbstract:Purpose – To investigate and identify the patterns of interaction between searchers and search engine during web searching. Design/methodology/approach – The authors examined 2,465,145 interactions from 534,507 users of Dogpile.com submitted on May 6, 2005, and compared Query Reformulation patterns. They investigated the type of Query modifications and Query modification transitions within sessions. Findings – The paper identifies three strong Query Reformulation transition patterns: between specialization and generalization; between video and audio, and between content change and system assistance. In addition, the findings show that web and images content were the most popular media collections. Originality/value – This research sheds light on the more complex aspects of web searching involving Query modifications.
Maarten De Rijke - One of the best experts on this subject based on the ideXlab platform.
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do topic shift and Query Reformulation patterns correlate in academic search
European Conference on Information Retrieval, 2017Co-Authors: Maarten De RijkeAbstract:While it is known that academic searchers differ from typical web searchers, little is known about the search behavior of academic searchers over longer periods of time. In this study we take a look at academic searchers through a large-scale log analysis on a major academic search engine. We focus on two aspects: Query Reformulation patterns and topic shifts in queries. We first analyze how each of these aspects evolve over time. We identify important Query Reformulation patterns: revisiting and issuing new queries tend to happen more often over time. We also find that there are two distinct types of users: one type of users becomes increasingly focused on the topics they search for as time goes by, and the other becomes increasingly diversifying. After analyzing these two aspects separately, we investigate whether, and to which degree, there is a correlation between topic shifts and Query Reformulations. Surprisingly, users’ preferences of Query Reformulations correlate little with their topic shift tendency. However, certain Reformulations may help predict the magnitude of the topic shift that happens in the immediate next timespan. Our results shed light on academic searchers’ information seeking behavior and may benefit search personalization.
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ECIR - Do Topic Shift and Query Reformulation Patterns Correlate in Academic Search
Lecture Notes in Computer Science, 2017Co-Authors: Maarten De RijkeAbstract:While it is known that academic searchers differ from typical web searchers, little is known about the search behavior of academic searchers over longer periods of time. In this study we take a look at academic searchers through a large-scale log analysis on a major academic search engine. We focus on two aspects: Query Reformulation patterns and topic shifts in queries. We first analyze how each of these aspects evolve over time. We identify important Query Reformulation patterns: revisiting and issuing new queries tend to happen more often over time. We also find that there are two distinct types of users: one type of users becomes increasingly focused on the topics they search for as time goes by, and the other becomes increasingly diversifying. After analyzing these two aspects separately, we investigate whether, and to which degree, there is a correlation between topic shifts and Query Reformulations. Surprisingly, users’ preferences of Query Reformulations correlate little with their topic shift tendency. However, certain Reformulations may help predict the magnitude of the topic shift that happens in the immediate next timespan. Our results shed light on academic searchers’ information seeking behavior and may benefit search personalization.
Min Zhang - One of the best experts on this subject based on the ideXlab platform.
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translating embeddings for modeling Query Reformulation
China Conference on Information Retrieval, 2018Co-Authors: Rongjie Cai, Yiqun Liu, Min ZhangAbstract:Query Reformulation understanding is important for Information Retrieval (IR) tasks, such as search results reranking and Query recommendation. Conventional works rely on the textual content of queries to understand Reformulation behaviors, which suffer from data sparsity problems. To address this issue, We propose a novel method to efficiently represent the behaviors of Query Reformulation by the translating embedding from the original Query to its reformulated Query. We utilize two-stage training algorithm to make the learning of multilevel intentions representation more adequate. We construct a new corpus of shopping search Query log and create a Query Reformulation graph based on this dataset. Referring to knowledge graph embedding methods, we use the accuracy of intentions prediction to evaluate experimental results. Our final result, an increase of 20.6% of the average prediction accuracy in 21 intentions, shows significant improvement compared to baselines.
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CCIR - Translating Embeddings for Modeling Query Reformulation
Lecture Notes in Computer Science, 2018Co-Authors: Rongjie Cai, Yiqun Liu, Min ZhangAbstract:Query Reformulation understanding is important for Information Retrieval (IR) tasks, such as search results reranking and Query recommendation. Conventional works rely on the textual content of queries to understand Reformulation behaviors, which suffer from data sparsity problems. To address this issue, We propose a novel method to efficiently represent the behaviors of Query Reformulation by the translating embedding from the original Query to its reformulated Query. We utilize two-stage training algorithm to make the learning of multilevel intentions representation more adequate. We construct a new corpus of shopping search Query log and create a Query Reformulation graph based on this dataset. Referring to knowledge graph embedding methods, we use the accuracy of intentions prediction to evaluate experimental results. Our final result, an increase of 20.6% of the average prediction accuracy in 21 intentions, shows significant improvement compared to baselines.
