Search results

The purpose of this research is to discuss the methodological aspects of a study, the results of which are detailed in a second paper.

Indices of search goal revision (or redefinition) were constructed based on the chronological coding of queries logged on the OCLC FirstSearch service. Queries within search sessions were coded according to their conceptual relationships, based on a set of rules designed to identify possible revisions of search goals, as well as strategic reformulations. Given the same user interface, similar types of user, and large datasets, it was assumed that the number of strategic reformulations would be reasonably uniform across databases, so that the indices could be regarded as indicators of relative amounts of goal revision.

The reliability of the coding used to construct the indices of goal redefinition was tested through parallel coding, and confirmed. Differences between the indices for various databases, on the other hand, were found to be statistically significant, allowing for the conclusion that the indices were able to show differences in the amount of goal redefinition occurring, given the assumption of similar levels of strategic reformulations across databases.

Transaction log analysis has mostly focused on syntactic issues; few analyses have examined the semantic content of queries or contextualised individual queries as part of an evolving search session. However, this paper shows that although transaction log analysis is a relatively indirect means of examining users' thoughts and intentions during their interaction with an information retrieval system, logs that provide sufficiently rich data may, in certain controlled situations, be used to provide deeper insights into information retrieval behaviour.

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 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.

This 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.

As 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.

Studies an important dimension of user behavior in information retrieval.

The purpose of this paper is to determine the digital library usage patterns as a means of improving the system, as well as the user experience, to give appropriate recognition to the most popular dissertations’ authors and to measure the interest of non-academic users for dissertations defended at the University of Novi Sad (UNS).

A logging module of the digital library of theses and dissertations of University of Novi Sad (PHD UNS) application has been implemented. The module recorded the messages relating to the search queries and downloads over a three-year period from 2017–2019. These logs are analysed using the Elasticsearch, Logstash and Kibana (ELK) technology stack and the results are shown using graphs and tables.

The analysis determined the perfect time for weekly maintenance of the system, defined a recommendation for improving the system and revealed the most popular dissertations. A significant number of downloads and queries originated from citizens, i.e. users outside the academic community.

The conducted analysis defined recommendations for the system improvement which can be used by PHD UNS research and development (R&D) team and revealed the most popular dissertations which are used for the promotion of its authors through faculties’ websites.

To the best of the authors’ knowledge, this is the first study of ELK based log analysis of a Serbian language documents’ repository. Besides, the value of results for the PHD UNS R&D team and UNS rector team, the study proves that PhD digital library presents an important Open Science communication channel for presenting scientific results to the citizens.

This study aims to investigate and compare mobile and desktop user search behaviours of the 1300K site, a Korean shopping search engine, by using transaction log analysis.

Transaction logs of 1300K site were collected over a three months’ period, from 1 January to 31 March 2018. The data set of this study consists of 1,149,690 desktop queries, 2,346,938 mobile queries, 2,481,747 desktop browsing activities and 2,550,309 mobile browsing activities. This study quantitatively analyses transaction log of 1300K site.

The results of this study show that mobile usage is higher than desktop usage: there are more mobile sessions than desktop sessions and the number of mobile queries is more than double of desktop queries. Overall, mobile query search behaviours are more simple, targeted and focused than desktop query search behaviours. Also, mobile browsing behaviours are more simple and passive than desktop browsing behaviours. However, mobile click behaviours are more active than desktop click behaviours.

To the best of the authors’ knowledge, this study appears to be the first of its type in Korea that compared search behaviours of a large number of users on desktop computers and mobile phones. To identify various characteristics of user search behaviours, this study analyses users’ directory browsing behaviour and click behaviour as well as query search behaviour. The results of this study can be implemented to address the effective improvement and development of search services and interfaces for different devices.

This work aims to investigate the sensitivity of ranking performance with respect to the topic distribution of queries selected for ranking evaluation.

The authors reweight queries used in two TREC tasks to make them match three real background topic distributions, and show that the performance rankings of retrieval systems are quite different.

It is found that search engines tend to perform similarly on queries about the same topic; and search engine performance is sensitive to the topic distribution of queries used in evaluation.

Using experiments with multiple real‐world query logs, the paper demonstrates weaknesses in the current evaluation model of retrieval systems.

With an explosion of datasets available on the Web, dataset search has gained attention as an emerging research domain. Understanding users' dataset behaviour is imperative for providing effective data discovery services. In this paper, the authors present a study on users' dataset search behaviour through the analysis of search logs from a research data discovery portal.

Using query and session based features, the authors apply cluster analysis to discover distinct user profiles with different search behaviours. One particular behavioural construct of our interest is users' expertise that the authors generate via computing semantic similarity between users' search queries and the title of metadata records in the displayed search results.

The findings revealed that there are six distinct classes of user behaviours for dataset search, namely; Expert Research, Expert Search, Expert Explore, Novice Research, Novice Search and Novice Explore.

The user profiles are derived based on analysis of the search log of the research data catalogue in this study. Further research is needed to generalise the user profiles to other dataset search settings. Future research can take on a confirmatory approach to verify these user groups and establish a deeper understanding of their information needs.

The findings in this paper have implications for designing search systems that tailor search results matching the diverse information needs of different user groups.

We propose for the first time a taxonomy of users for dataset search based on their domain expertise and search behaviour.

Users' 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.

Qualitative 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.

A 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.

Data 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.

A 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.

The 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.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0389

The work presented in this paper aims to provide an approach to classifying web logs by personal properties of users.

The authors describe an iterative system that begins with a small set of manually labeled terms, which are used to label queries from the log. A set of background knowledge related to these labeled queries is acquired by combining web search results on these queries. This background set is used to obtain many terms that are related to the classification task. The system then ranks each of the related terms, choosing those that most fit the personal properties of the users. These terms are then used to begin the next iteration.

The authors identify the difficulties of classifying web logs, by approaching this problem from a machine learning perspective. By applying the approach developed, the authors are able to show that many queries in a large query log can be classified.

Testing results in this type of classification work is difficult, as the true personal properties of web users are unknown. Evaluation of the classification results in terms of the comparison of classified queries to well known age‐related sites is a direction that is currently being exploring.

This research is background work that can be incorporated in search engines or other web‐based applications, to help marketing companies and advertisers.

This research enhances the current state of knowledge in short‐text classification and query log learning.

This paper aims to examine the behaviour of the Egyptian scholars while accessing electronic resources through two federated search tools. The main purpose of this article is to provide guidance for federated search tool technicians and support teams about user issues, including the need for training.

Log files were exploited to examine the behaviour of users of information retrieval systems. This study examined two log files extracted from federated search tools available to the Egyptian scholars' community for accessing electronic resources. A data mining approach was implemented to investigate user behaviour through deep analysis of these logs.

Results show that: none of the available tools provide error messages for dummy queries; most of the Egyptian scholars had short queries; Boolean operators are not used in about 50 per cent of the queries; federated search tools do not provide techniques for query reformation; the optimal days for system maintenance are the non‐weekend vacations; and early morning is the best time for maintenance.

To maximise the value of the federated search tools by understanding user trends when utilising federated search tools. The study shows that more attention should be given to the search capabilities through ongoing training and awareness in order to maximise the benefit from the available resources and tools.

The hypothetical value of the federated search tools has not been previously examined and analysed to understand user trends.

This study is an attempt to develop an automatic identification method for Arabic web queries and divide them into several query types using data mining. In addition, it seeks to evaluate the impact of the academic environment on using the internet.

The web log files were collected from one of the higher institute's servers over a one‐month period. A special program was designed and implemented to extract web search queries from these files and also to automatically classify Arabic queries into three query types (i.e. Navigational, Transactional, and Informational queries) based on predefined specifications for each type.

The results indicate that students are slowly and gradually using the internet for more relevant academic purposes. Tests showed that it is possible to automatically classify Arabic queries based on query terms, with 80.6 per cent to 80.2 per cent accuracy for the two phases of the test respectively. In their future strategies, Jordanian universities should apply methods to encourage university students to use the internet for academic purposes. Web search engines in general and Arabic search engines in particular may benefit from the proposed classification method in order to improve the effectiveness and relevancy of their results in accordance with users' needs.

Studying internet web logs has been the subject of many papers. However, the particular domain, and the specific focuses on this research are what can distinguish it from the others.