Query Reformulation Patterns Research Articles

Remote OPAC users’ search query reformulation (SQR) patterns: a transaction log analysis

PurposeUsers' search logs are implicit feedbacks on how searchers interact with online information retrieval (IR) systems. The purpose of this paper is to analyze search query reformulation (SQR) patterns of University of Dar es Salaam remote OPAC users.Design/methodology/approachQualitative and quantitative analysis of transaction logs were employed to ascertain the characteristics of search queries and the patterns in which remote OPAC users reformulate their search queries. The study covered a period of six months, commencing from January to June 2019.FindingsA total of 30,474 search hits were submitted by remote OPAC users during the period under study. Individuals from academic and research institutions, computing consortia, and telecommunication companies are the main users of the system. Most of the searches originated from North America and Europe, with few searches coming from China and India. Besides improving search results, SQRs are linked with the existence of multiple information demands as manifested by the use of heterogeneous headwords within individual search episodes.Research limitations/implicationsData collected covered only six months. Similarly, it was however not possible to analyze users' search query formulation within specific contexts such as task-based information searching.Practical implicationsA query recommendation system should be integrated into the OPAC functionalities to improve users' search experiences. Alternatively, there should be a migration to a new system that offers more advanced search features and functionalities.Originality/valueThe study has contributed new insights in SQR studies particularly on how non-institutional affiliated users translate their information needs into search queries during information searching processes.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0389

Mixed language queries in online searches

PurposeWith the increasing number of online multilingual resources, cross-language information retrieval (CLIR) has drawn much attention from the information retrieval (IR) research community. However, few studies have examined how and why multilingual searchers seek information in two or more languages, specifically how they switch and mix language in queries to get satisfying results. The purpose of this paper is to focus on Chinese–English bilinguals’ intra-sentential code-switching behaviors in online searches. The scenarios and reasons of code-switching, factors that may affect code-switching, the patterns of mixed language query formulation and reformulation and how current IR systems and other search tools can facilitate such information needs were examined.Design/methodology/approachIn-depth semi-structured interviews were used as the research method. In total, 30 participants were recruited based on their English proficiency, location and profession, using a purposive sampling method.FindingsFour scenarios and four reasons for using Chinese–English mixed language queries to cover information needs were identified, and results suggest that linguistic and cultural/social factors are of equivalent importance in code-switching behaviors. English terms and Chinese terms in queries play different roles in searches, and mixed language queries are irreplaceable by either single language queries or other search facilitating features. Findings also suggest current search engines and tools need greater emphasis in the user interface and more user education is required.Originality/valueThis study presents a qualitative analysis of bilinguals’ code-switching behaviors in online searches. Findings are expected to advance the theoretical understanding of bilingual users’ search strategies and interactions with IR systems, and provide insights for designing more effective IR systems and tools to discover multilingual online resources, including cross-language controlled vocabularies, personalized CLIR tools and mixed language query assistants.

Characterizing queries in cross-device OPAC search: a large-scale log study

PurposeThe study focused on online public access catalog (OPAC) users’ cross-device search behavior. The purpose of this paper is to understand the characteristics of cross-device OPAC searches, and to identify query reformulation (QR) patterns during device transitions.Design/methodology/approachThe transaction log from a university library, spanning six months, was used to conduct the quantitative analysis. The query vocabulary richness, which refers to the average number of unique words each query contains in a search session, can evaluate query diversity, and contribute to the analysis of QR.FindingsThe results show that PC-PC transition is the most important pattern of device transition. The time interval of device transition was different to the time interval of transitions in web searches. Short device transitions mainly occurred in daytime, and the number of transitions that occurred in less than one minute was higher than on the web. Searches for Industry and Technology triggered the most device transitions, and the users tended to choose the same search field. In addition, the authors made a detailed analysis of the reasons for same-type device transitions and different-type device transitions. Furthermore, the authors focused on the characteristics of adjacent QR patterns. The authors not only refined the concept of cross-device to include the same-type device transition, but also summarized the characteristics of the cross-device QR patterns, which can be used to predict post-switch queries.Originality/valueThis study extends research into cross-device interaction and cross-device search to the domain of digital library research. The authors also introduced QR perspective on cross-device interaction on OPAC.

Impact of device on search pattern transitions

PurposeThis paper discusses the differences in search pattern transitions for mobile phone, tablet and desktop devices by mining the transaction log data of a library online public access catalogue (OPAC). We aimed to analyze the impacts of different devices on user search behavior and provide constructive suggestions for the development of library OPACs on different devices.Design/methodology/approachBased on transaction logs which are 9 GB in size and contain 16,140,509 records of a university library OPAC, statistics and clustering were used to analyze the differences in search pattern transitions on different devices in terms of two aspects: search field transition patterns and query reformulation patterns.FindingsSearch field transition patterns are influenced by the input function and user interfaces of different devices. As reformulation times increase, the differences between query reformulation patterns among different devices decrease.Practical implicationsMobile-side libraries need to optimize user interfaces, for example by setting web page labels and improving input capabilities. Desk-side libraries can add more suggestive content on the interface.Originality/valueUnlike previous studies, which have analyzed web search, this paper focuses on library OPAC search. The search function of mobile phones, tablets and desktops were found to be asymptotic, which was a good illustration of how devices have a large impact on user search behavior.

An investigation of the query behavior in task‐based collaborative exploratory Web search

AbstractCollaboration in the information seeking and retrieval environment is common, particularly when the search task is complex and exploratory. Multiple factors such as contextual features and task type can affect users' query behavior. This paper presents a study investigating the effects of collaboration and task types on users' query behavior. The study involves two conditions: collaborative search and individual search, and the two search tasks: the recall‐oriented information‐gathering and the utility‐based decision‐making. We analyze users' query behavior in three dimensions: basic query features (e.g. the number of queries), query reformulation patterns (e.g. New, Specification, Generalization and Reconstruction) and query performance. The findings of this study reveal that queries are more diverse in collaborative search and recall‐oriented tasks. Users employed New and Specialization more often as query reformulation types in collaborative search while people in individual search use Reconstruction more often. Besides, the successful query rate is higher in individual search and recall‐oriented tasks.

Neural network applications for automatic new topic identification of FAST and Excite search engine transaction logs

Abstract: Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms for more efficient search engines. Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. This study proposes an artificial neural network application in the area of search engine research to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals and query reformulation patterns. Sample data logs from the FAST and Excite search engines are selected to train the neural network and then the neural network is used to identify topic changes in the data log. As a result, almost all the performance measures yielded favourable results.

Query reformulation mining: models, patterns, and applications

Understanding query reformulation patterns is a key task towards next generation web search engines. If we can do that, then we can build systems able to understand and possibly predict user intent, providing the needed assistance at the right time, and thus helping users locate information more effectively and improving their web-search experience. As a step in this direction, we build a very accurate model for classifying user query reformulations into broad classes (generalization, specialization, error correction or parallel move), achieving 92% accuracy. We then apply the model to automatically label two very large query logs sampled from different geographic areas, and containing a total of approximately 17 million query reformulations. We study the resulting reformulation patterns, matching some results from previous studies performed on smaller manually annotated datasets, and discovering new interesting reformulation patterns, including connections between reformulation types and topical categories. We annotate two large query-flow graphs with reformulation type information, and run several graph-characterization experiments on these graphs, extracting new insights about the relationships between the different query reformulation types. Finally we study query recommendations based on short random walks on the query-flow graphs. Our experiments show that these methods can match in precision, and often improve, recommendations based on query-click graphs, without the need of users' clicks. Our experiments also show that it is important to consider transition-type labels on edges for having recommendations of good quality.

이미지 검색 과정에 나타난 질의 전환 및 재구성 패턴에 관한 연구

The purpose of this study is to investigate image search query reformulation patterns in relation to image attribute categories. A total of 592 sessions and 2.445 queries from the Excite Web search engine log data were analyzed by utilizing Batley's visual information types and two facets and seven sub-facets of query reformulation patterns. The results of this study are organized with two folds query reformulation and categorical transition. As the most dominant categories of queries are specific and general/nameable, this tendency stays over various search stages. From the perspective of reformulation patterns, while the Parallel movement is the most dominant, there are slight differences depending on initial or preceding query categories. In examining categorical transitions, it was found that 60-80% of search queries were reformulated within the same categories of image attributes. These findings may be applied to practice and implementation of image retrieval systems in terms of assisting users' query term selection and effective thesauri development.

Patterns of query reformulation during Web searching

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 stud...

Patterns of query reformulation during Web searching

AbstractQuery 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.

Markovian analysis for automatic new topic identification in search engine transaction logs

AbstractTopic analysis of search engine user queries is an important task, since successful exploitation of the topic of queries can result in the design of new information retrieval algorithms for more efficient search engines. Identification of topic changes within a user search session is a key issue in analysis of search engine user queries. This study presents an application of Markov chains in the area of search engine research to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals, query reformulation patterns and the continuation/shift status of the previous query. The findings show that Markov chains provide fairly successful results for automatic new topic identification with a high level of estimation for topic continuations and shifts. Copyright © 2009 John Wiley & Sons, Ltd.

Defining a session on Web search engines

AbstractDetecting query reformulations within a session by a Web searcher is an important area of research for designing more helpful searching systems and targeting content to particular users. Methods explored by other researchers include both qualitative (i.e., the use of human judges to manually analyze query patterns on usually small samples) and nondeterministic algorithms, typically using large amounts of training data to predict query modification during sessions. In this article, we explore three alternative methods for detection of session boundaries. All three methods are computationally straightforward and therefore easily implemented for detection of session changes. We examine 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005. We compare session analysis using (a) Internet Protocol address and cookie; (b) Internet Protocol address, cookie, and a temporal limit on intrasession interactions; and (c) Internet Protocol address, cookie, and query reformulation patterns. Overall, our analysis shows that defining sessions by query reformulation along with Internet Protocol address and cookie provides the best measure, resulting in an 82% increase in the count of sessions. Regardless of the method used, the mean session length was fewer than three queries, and the mean session duration was less than 30 min. Searchers most often modified their query by changing query terms (nearly 23% of all query modifications) rather than adding or deleting terms. Implications are that for measuring searching traffic, unique sessions may be a better indicator than the common metric of unique visitors. This research also sheds light on the more complex aspects of Web searching involving query modifications and may lead to advances in searching tools.

Automatic new topic identification in search engine transaction logs

PurposeContent analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms of search engines, which can offer custom‐tailored services to the web user. Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. The purpose of this study is to address these issues.Design/methodology/approachThis study applies genetic algorithms and Dempster‐Shafer theory, proposed by He et al., to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals and query reformulation patterns. A sample data log from the Norwegian search engine FAST (currently owned by overture) is selected to apply Dempster‐Shafer theory and genetic algorithms for identifying topic changes in the data log.FindingsAs a result, 97.7 percent of topic shifts and 87.2 percent of topic continuations were estimated correctly. The findings are consistent with the previous application of the Dempster‐Shafer theory and genetic algorithms on a different search engine data log. This finding could be implied as an indication that content‐ignorant topic identification, using query patterns and time intervals, is a promising line of research.Originality/valueStudies an important dimension of user behavior in information retrieval.

Internet usage transaction log studies: The next generation

Internet usage transaction log studies: The next generation

Use of query reformulation and relevance feedback by Excite users

Examines the use of query reformulation, and particularly the use of relevance feedback by users of the Excite Web search engine. A total of 985 user search sessions from a data set of 18,113 user search sessions containing 51,473 queries were examined. Includes a qualitative and quantitative analysis of 191 user sessions including more than one query, to examine patterns of user query reformulation; and second, all 804 user sessions including relevance feedback were examined. Results show limited use of query reformulation and relevance feedback by Excite users – only one in five users reformulated queries. Most relevance feedback sessions were successful. Identifies the most common pattern of searching and discusses implications for Web search system design.