User Search Research Articles

A One Stop Solution Focusing on Tourism

Abstract: For travelers, the most frustrating task is planning a well-organized and budget-friendly trip. Even through travel agencies provide ready-made schedules, they cannot cater to the needs of every traveler. Most people would like to have a relaxing and enjoyable holiday, but the complicated process of planning a trip usually results in cancellations, which makes it hard to bring travel dreams into reality. This would simply be a process through the integration of multiple important services within one convenient solution in All-InOne Tourism. Some features in the platform are the ability for users to login and register, which gives them an opportunity to create their personal accounts and manage them. The platform is able to help users search places, find cabs, book hotels, and get events around the place within a single application. Also, the application has a feedback section in which user can share their experience and ideas. This platform makes use of Django for development of back-end; it is a powerful web framework, which ensures smooth and secure performance. The front-end is done in HTML and CSS to create an easy-to use, aesthetic interface. Combining all these technologies, All-In-One tourism streamlines travel planning, saving time for travelers and making their travels easier to organize and enjoy.

The Impact of Color Combination on Visual Search Efficiency and User Experience in Human-Machine Interface: A Case Study of Metro Electronic Guide Screen

Color is an important attribute of human-perceived human-machine interface (HMI), and it is unclear which color combinations produce the best performance and user experience in visual search tasks. Taking the metro electronic guide screen (MEGS) as an example, the eye-tracking (E-T) device is used to record the pupil diameter and search time, and the Likert scale is used to evaluate the satisfaction and usability, and the influence of the four factors of background hue, saturation, brightness and text color in HMI on user search efficiency and user experience is discussed in depth. The results show that the recognition efficiency is best when the background hue is medium brightness, medium saturation green with white text color. Meanwhile, the results of user satisfaction and ease of use are basically consistent with the objective experimental results, and there is a certain correlation between the subjective preference of color combination and visual performance.

A Study on Employees Perception Towards Online Job Portals with Reference to SRM Group

This Abstract discusses the importance of employees' perceptions towards online job portals, shedding light on their preferences, usage patterns, and suggestions for improvement. Throughan analysis of 180 respondents, it is revealed that online job portals play a crucial role in the job search process, with platforms like LinkedIn and Naukri being favored for their reliabilityand relevance. Despite a high usage rate and overall positive attitudes, concerns regarding security and privacy remain significant. Additionally, the study identifies gender-based differences in perceived career opportunities and highlights the importance of continuous improvement in user interface, search functionality, and security measures to meet evolving user needs. Ultimately, this research underscores the importance of understanding employee perceptions to optimize online job portals and enhance their efficacy in facilitating successful job searches. Furthermore, the study sheds light on the prevalent usage patterns among employees, highlighting the pivotal role of online job portals in facilitating job searches and career advancement. Specific platforms like LinkedIn and Naukri emerge as preferred choices due to their reliability and relevance, while also serving as valuable resources for career development. Interestingly, gender-based differences in perceptions of career opportunities areidentified, suggesting nuanced considerations in platform design and functionality. Despite overall satisfaction, the study underscores the continuous need for improvement, particularly in user interface, search functionality, and security measures, to align with evolving employeeneeds and preferences in the dynamic job market landscape

Spoken conversational search: Evaluating the effect of system clarifications on user experience through Wizard‐of‐Oz study

AbstractPrior research in human–computer interaction suggests that system‐level clarifications are necessary for understanding user intent and communicating effectively with the user. Such clarifications or explanations could contain the system's abstract knowledge of the search or a functional description of the search process (queries and information sources employed). While these interactions may aid the user and the agent in better understanding each other, very few studies have explored the influence of such clarifications on the users' search experience. This research examines whether and how system‐level clarifications (or explanations) affect the user experience when searching through spoken dialogues. We analyzed user satisfaction and preferences in systems with and without explicit clarifications in a within‐subjects Wizard‐of‐Oz user study. We recruited 25 participants and collected user–system interaction data for 50 search sessions. The user feedback was collected using pre‐ and post‐task surveys and exit interviews. Statistical and qualitative analysis of user responses yielded some interesting findings. While Wilcoxon Signed Rank Test found that using explicit system‐level clarifications had no positive influence on the user's search experience, the overall search experience degraded with system clarifications (Z = −2.066, p = 0.04). The user interview data provided valuable insights into how and when clarifications should be offered to the user.

The algorithm for selecting publications on a given topic considering keyword priorities

The article investigates the problems that exist in existing search engines for scientific publications. The search algorithms used in various search engines for scientific publications are described. The aim of the article is to develop a method for selecting publications on a given topic based on assessing the relevance of keyword sets. A review of the literature that was analyzed during the research is presented. Among the publications studied were materials related to the theory of set similarity, namely the use of the Jacquard coefficient and editing distance. A measure for determining the similarity of keyword sets is presented, which is based on the Jacquard coefficient taking into account the weighting coefficients of keywords. An algorithm is presented that can be used to determine the degree of similarity of publications to a user's search query based on keyword sets with weighting coefficients. The algorithm is based on the measure of similarity presented by us and the editing distance presented by us. The algorithm can be used to rank search results in search engines for scientific publications, as well as to compare the efficiency of different search engines, assess the quality of the results they return. The algorithm can also be used in book and film recommendation systems based on user preferences. The article provides the pseudocode of the algorithm. It is demonstrated on a limited data set how the measure calculated by the algorithm changes depending on the distribution of keyword weights in the user's query and the number of keywords.

ANALISIS PREFERENSI PENGGUNA TERHADAP HARGA DAN KEMUDAHAN PENGGUNAAN DALAM BERTRANSAKSI PRODUK KECANTIKAN PADA APLIKASI SHOPEE

This research aims to understand Shopee app users' preferences regarding price and ease of use of the app in determining purchasing decisions for beauty products. The research method used is descriptive quantitative, utilizing primary data through the distribution of online questionnaires to 675 students from the Faculty of Economics and Business, class of 2022. From the total number of students, there were 125 respondents, and only 54 respondents met the sample criteria. The research results show that respondents' preferences for the Price variable yielded a "good" rating with an average score of 4.15, as the prices on the Shopee app are considered more competitive than similar apps. For the user-friendliness variable, respondents' preferences yielded a "good" rating with an average score of 4.16 due to the ease of product search, payment system options, and Shopee displaying recommended products based on user search results. This research reinforces other studies regarding the influence of price and ease of use of the Shopee application on purchasing decisions, especially for beauty products.

Formulating standard requirements for search web-based interfaces of e-catalogs

Within the framework of R&D project “Information support of research by scientists and specialists on the basis of RNPLST Open Archive as the system of scientific knowledge aggregation”, the RNPLS&T carries on the analysis of general characteristics and features of and defining standard requirements for the LIS search interfaces. The author attempts to develop the array of the requirements for modern web-based interfaces of e-catalogs, to classify and formulate them more specifically, and to substantiate these requirements. She proposes the following classification of the requirements: general requirements, requirements for search functionality structure, requirement for content enrichment, requirement for the system of search user support, requirements for applying recommendation system and existence of auxiliary user support. The author also suggests the ways to improve search interfaces of e-catalogs, in particular, to unify and differentiate search instruments, to obtain additional search options, to implement different types of user support more intensively, and to create effective multifunctional recommender systems. The proposed recommendations may be used for search interfaces of library information systems.

Cross-lingual Search Intent Understanding Framework Based on Multi-modal User Behavior

This paper proposes a novel cross-lingual search intent understanding framework leveraging multi-modal user behavior analysis. With the increasing complexity of network traffic and the diversity of user behaviors across languages, traditional approaches often struggle to capture and interpret user search intent in multilingual contexts accurately. Our framework integrates multiple behavioral signals, including query patterns, click sequences, and temporal dynamics, through a sophisticated neural tensor network architecture. The system employs a dual-encoder structure with shared parameters to maintain semantic consistency across languages while incorporating a dynamic behavior sequence learning mechanism to capture temporal dependencies. Experimental evaluation was conducted on a large-scale dataset comprising over 6 million user interactions across four language pairs (EN-ZH, EN-ES, EN-FR, EN-DE) collected over six months. The framework significantly improves over baseline methods, demonstrating an average cross-lingual accuracy of 0.923 and behavior prediction precision of 0.891. Ablation studies reveal the critical role of multi-head attention mechanisms and temporal modeling in maintaining system performance. The framework retains real-time processing capabilities with an average latency of 45ms per request under standard load conditions. Our research advances the field of cross-lingual information retrieval by introducing a practical approach to integrating behavioral signals with linguistic features, providing valuable insights for developing more sophisticated multilingual search systems.

Identifying the geographic leading edge of Lyme disease in the United States with internet searches: A spatiotemporal analysis of Google Health Trends data.

The geographic footprint of Lyme disease is expanding in the United States, which calls for novel methods to identify emerging endemic areas. The ubiquity of internet use coupled with the dominance of Google's search engine makes Google user search data a compelling data source for epidemiological research. We evaluated the potential of Google Health Trends to track spatiotemporal patterns in Lyme disease and identify the leading edge of disease risk in the United States. We analyzed internet search rates for Lyme disease-related queries at the designated market area (DMA) level (n = 206) for the 2011-2019 and 2020-2021 (COVID-19 pandemic) periods. We used maps and other exploratory methods to characterize changes in search behavior. To assess statistical correlation between searches and Lyme disease cases reported to Centers for Disease Control and Prevention (CDC) between 2011 and 2019, we performed a longitudinal ecological analysis with modified Poisson generalized estimating equation regression models. Mapping DMA-level changes in "Lyme disease" search rates revealed an expanding area of higher rates occurring along the edges of the northeastern focus of Lyme disease. Bivariate maps comparing search rates and CDC-reported incidence rates also showed a stronger than expected signal from Google Health Trends in some high-risk adjacent states such as Michigan, North Carolina, and Ohio, which may be further indication of a geographic leading edge of Lyme disease that is not fully apparent from routine surveillance. Searches for "Lyme disease" were a significant predictor of CDC-reported disease incidence. Each 100-unit increase in the search rate was significantly associated with a 10% increase in incidence rates (RR = 1.10, 95% CI: 1.07, 1.12) after adjusting for environmental covariates of Lyme disease identified in the literature. Google Health Trends data may help track the expansion of Lyme disease and inform the public and health care providers about emerging risks in their areas.

A Study on the Internet Attention of Non-heritage Tourism Performing Arts Products Based on Baidu Index --- Taking “Seeing Pingyao Again” as an Example

Cultural performing arts is an important manifestation of regional tourism development, and in the era of big data, analyzing the network attention of non-heritage cultural tourism performing arts products helps the dissemination of non-heritage culture and the healthy development of non-heritage tourism. Based on the Jitterbug Index platform, the user search index of Seeing Pingyao Again from January 2014 to December 2023 was retrieved, and the characteristics and trends of the overall search index, the PC search index and the mobile search index were analyzed, which in turn led to the proposal of relevant suggestions for the development of cultural tourism performing arts products.

Associating cognitive abilities with naturalistic search behavior

AbstractDifferences in cognitive abilities affect search behaviors, but this has mostly been observed in laboratory experiments. There is limited research on how users search for information in real‐world, naturalistic settings and how real‐world search behaviors relate to cognitive abilities. In this study, we investigated a wide range of behavioral data captured from real‐life search tasks, their association with users' cognitive abilities, and the potential for automatically inferring cognitive abilities from these data. Furthermore, we aimed to determine the data quantity and monitoring duration needed to effectively estimate cognitive abilities from naturalistic behavior. Twenty individuals with βvarying cognitive abilities participated in the experiments in which their everyday search behavior was continuously recorded for 14 days. Their cognitive ability was evaluated through standard tests conducted individually. Data consisted of over 800 h of monitoring, including 2022 queries extracted from 1,442,447 screen frames and associated operating system logs. Using these data, naturalistic search behaviors were associated with cognitive abilities, and predictive models were trained. The results showed that lower selective attention was found to be associated with longer dwelling on selected search results. Faster psychomotor speed and higher fluid intelligence were found to be associated with a greater amount of text read on selected pages. Predictive models exhibited small error rates in predicting cognitive abilities.

AI- and LLM-driven search tools: A paradigm shift in information access for education and research

The article reports on an exploratory study that assesses the results produced by emerging artificial intelligence (AI)- and large language model-driven search tools in response to a series of queries and prompts based on four scenarios of information-intensive tasks of university students and researchers. Sixteen questions and prompts were created based on four scenarios of information-intensive tasks of university students. Each of these questions and prompts was presented to six AI-driven search tools, and the results were manually checked to assess their suitability for specific user needs and contexts. Based on the findings, it was argued that while the AI-driven tools bring a paradigm shift in information access for education and research, outputs generated by these tools vary quite significantly. Choice of the right tool, framing the question and further prompting play a key role. Also, users need to scrutinise each output to check their quality and reliability in the context of the specific search tasks. It was concluded that further research is needed involving different user groups, scenarios and search tasks and different AI-driven search tools. Implications of the use of AI-driven search tools for libraries and scholarly databases, as well as for research and scholarship in different areas of information science, are discussed.

Indexing for Fine Arts Monographs: A Comparison Between User Queries and Controlled Vocabularies

ABSTRACTPrior research suggested the empirical approach to indexing for fine arts based on users' search queries. This study examined the characteristics of user queries submitted to the library catalog of a fine arts academy, and compared users' search terms with controlled vocabularies to assess their levels of matching. Based on search‐log analysis, this study identified eight categories of user queries and 10 subcategories, indicating that the information needs of the fine arts community are diverse in language and culture and focus on named entities, disciplines, and various types and levels of fine arts. The mapping between user queries and controlled vocabularies found that around 20% of user search terms were non‐matching. Based on the findings, this study provided suggestions for improving the search functions of library catalogs and enhancing controlled vocabularies to support the retrieval of fine arts monographs.

How to bridge the gap? Information asymmetry in Tibetan-Chinese bilingual search behavior

Significant information asymmetries exist between majority and minority languages on the web. The information asymmetry makes it difficult for ethnic minorities to access online information in their native languages, exacerbating their information vulnerability. It is necessary to investigate whether the information asymmetry between these two languages affects the language selection and interaction behavior of bilinguals in online searches. A user search experiment (N = 30) was conducted in Tibetan-Chinese bilinguals, combined with retrospective think-aloud interviews. Tibetan-Chinese bilinguals showed a preference for Chinese search, seen as a native language (L1) loss tendency in search. The bilinguals present an information asymmetry stereotype, which influenced their search language selection and negatively affected their search behavior. Four search phases for Tibetan-Chinese bilinguals were identified: direct search, vigilant avoidance, blind attempts, and serendipitous search.

Harnessing AI for Enhanced Searching in Digital Libraries: Transforming Research Practices

The application of Artificial Intelligence (AI) technologies in digital libraries is changing the paradigm in which users search and interact with digital content. This article is focused on how the use of AI technologies improves the search in digital libraries, demonstrating the benefits of such technologies in overcoming the challenges associated with formulation of query, information retrieval, and end user usage. Most search engine systems face the problems of user queries, which are inherent in online searches. Many user queries are vague, imprecise or ambiguous with regards to the context and as a result lead to less than satisfactory search results. AI technologies such as Natural Language Processing (NLP) and Machine Learning (ML) solve this challenge effectively as they enhance comprehension of queries, contextual understanding, and relevance of the results returned. With the capabilities of NLP incorporated in search engines, search queries which are in the form of natural language can be understood and executed which leads to efficiency in targeting the information needed. Comprehensive restructuring of standard information systems is used by computer learning algorithms for searches by inference and instills usage in users. This study also examines the place of AI in query formulation and the different ways it can be employed; fully automated, semi-automated and/or manually so that the users are less faced with the challenges that are usually present during the query formulation process. Such increases in the level of difficulty and scope of information retrieval suggest that search AI improved the scenario.

User-centred collection metadata: from images as information to facilitating socio-affective connections

PurposeAs museums and other memory institutions continue to invest considerably in mass-digitising collections and participating in large search portals, it is essential to understand existing and potential users, their motivations and search needs to inform collections’ documentation. In this article, we discuss insights from a collaborative project with National Museums Scotland, set up to enhance the findability of collection images and inform documentation practices by understanding the collections users and their search terms.Design/methodology/approachThe research involved interviews with National Museums Scotland staff, users and non-users of the Museums’ Search our Collections portal encompassing a concept mapping and card sort activity; online surveys and content analysis of user search queries.FindingsThe project revealed that participants are interested in searching the online collections by terms often not represented in collections metadata, including terms related to identity (their own but also others’) and social context (e.g. through seasonal and social events); emotional and sensory interests (e.g. visual characteristics) and narrative themes (e.g. on under-represented histories).Originality/valueBased on the findings, we further theorise the semantic gap in online museum collection metadata. To bridge this gap and cater to how users search, we argue for a paradigm shift in documentation practices: suggesting practitioners should not only view collection images as information but also tap into their rich potential for enabling social and affective connections.

Marketplace Data Analysis Using Web Scraping

The rapid development of the internet in fast communication has provided significant benefits for business people, allowing them to offer and sell product information boldly. One of the main advantages is the ability to search and find a variety of products easily through the internet. This convenience has led to the proliferation of many online stores in Indonesia, so that the need for applications that can help users search and combine product data from the internet arises. This study aims to develop a web application for product data analysis by utilizing web scraping techniques on the Tokopedia marketplace website, using the BeautifulSoup4 (Bs4) method. The web application is designed using the Streamlit module from Python. The results of this study are web applications that process data analysis taken from the Tokopedia website, which are classified based on certain criteria such as price range, highest and lowest ratings, highest and lowest sales, highest and lowest prices, percentage of location distribution, percentage of seller stores, and product recommendations.

Utilizing Apache Jena Fuseki for Ontology-Based Smartphone Knowledge Representation

Background: Smartphones are a fundamental require for everybody since smartphones can offer assistance someone's work through different highlights and certain innovation contained within the smartphone. Some people's need for information about smartphones makes people confused in choosing smartphone products because there are many smartphone brands available on the market, as a result, many people still buy smartphones that do not suit their needs and preferences. That is why ontology-based knowledge representation is becoming increasingly important in the field of smartphone technology to improve data organization, data retrieval, and interoperability. Objective: This research aims to develop a smartphone ontology using the Apache Jena Fuseki server which functions as a data collection tool and facilitates knowledge management about smartphones. Methods: This ontology was built using the methontology method, namely an ontology development method that is superior in providing a detailed description of each required activity. This smartphone ontology was developed using the Protégé 5.5.0 application which consists of 4 classes, 9 object properties, 15 data properties, and 92 individuals. Results: The research results show that the ontology built can help users search for smartphones that suit their criteria and needs. This research also succeeded in developing an android and semantic web-based application that allows users to search for smartphones more easily and efficiently, strengthening the benefits of the developed ontology in supporting smartphone purchasing decisions. Conclusion: The contribution of this research is to help customers, by providing recommendation the smartphone that best meets the requirements or best fits the given knowledge representation.

Towards forward secure verifiable data streaming with support for keyword query

Verifiable data streaming (VDS) allows users to continuously upload their encrypted data items to untrusted cloud servers to reduce the burden of local storage. Meanwhile, it enables users to update outsourced data and initiate queries to verify data integrity. However, most previous works focus on queries for a certain index and are not compatible with general keyword queries. To this end, we propose a VDS protocol that supports keyword queries. Specifically, we first present an efficient dynamic symmetric searchable encryption (DSSE) with range query, which can represent multiple queried keywords within a range as the concise query token, thereby achieving communication-optimized keyword queries while achieving unbounded data appending. Furthermore, we construct a new VDS protocol supporting keyword queries by integrating the proposed range DSSE. Specifically, suppose the user searches for multiple keywords within a certain range. In that case, cloud servers can match the corresponding data items based on the user's query token, and users can verify the integrity of these data items. Security analysis demonstrates that our VDS protocol achieves forward security. Experimental results show that it sacrifices acceptable costs in exchange for keyword retrieval capability.

Influence of Breast Cancer Awareness Month on Public Interest of Breast Cancer in High-Income Countries Between 2012 and 2022: Google Trends Analysis.

Breast cancer is the most common cancer among women worldwide. High-income countries have a greater incidence and mortality rate of breast cancer than low-income countries. As a result, raising awareness about breast cancer is crucial in increasing the chances of early detection and treatment. Social media has evolved into an essential tool for Breast Cancer Awareness Month campaigns, allowing people to share their breast cancer stories and experiences while also providing a venue for education and support. The aim of this study was to assess the level of public interest in searches linked to breast cancer among a sample of high-income nations with a sizable internet user base from 2012 to 2022. We also sought to compare the proportional search volume for breast cancer during Breast Cancer Awareness Month with that during other months of the year. Google Trends was used to retrieve data on internet user search behaviors in the context of breast cancer from 2012 to 2022. Seven countries were evaluated in this study: Australia, Canada, Ireland, New Zealand, the United Kingdom, Saudi Arabia, and the United States, in addition to global data. Breast cancer relative search volume trends were analyzed annually, monthly, and weekly from 2012 to 2022. The annual percent change (APC) was calculated for each country and worldwide. Monthly and weekly data were used to identify potential trends. A fluctuating pattern in APC rates was observed, with a notable increase in 2018 and a significant decrease in 2020, particularly in Saudi Arabia. Monthly analysis revealed a consistent peak in search volume during October (Breast Cancer Awareness Month) each year. Weekly trends over a 20-year period indicated significant decreases in Australia, Canada, New Zealand, and the United States, while increases were noted in Ireland. Heatmap analysis further highlighted a consistent elevation in median search volume during October across all countries. These findings underscore the impact of Breast Cancer Awareness Month and suggest potential influences of governmental COVID-19 pandemic control measures in 2020 on internet search behavior.