User Search Research Articles

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.

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.

Utilizing Ant Colony Optimization for Result Merging in Federated Search

Federated search or distributed information retrieval routes the user's search query to multiple component collections and presents a merged result list in ranked order by comparing the relevance score of each returned result. However, the heterogeneity of the component collections makes it challenging for the central broker to compare these relevance scores while fusing the results into a single ranked list. To address this issue, most existing approaches merge the returned results by converting the document ranks to their ranking scores or downloading the documents and computing their relevance score. However, these approaches are not efficient enough, because the former methods suffer from limited efficacy of result merging due to the negligible number of overlapping documents and the latter are resource intensive. The current paper addresses this problem by proposing a new method that extracts features of both documents and component collections from the available information provided by the collections at query time. Each document and its collection features are exploited together to establish the document relevance score. The ant colony optimization is used for information retrieval to create a merged result list. The experimental results with the TREC 2013 FedWeb dataset demonstrate that the proposed method significantly outperforms the baseline approaches.

Enhancing Image SEO using Deep Learning Algorithms: A Research Approach

Visual content influences profoundly user interactions and search engine rankings. Traditional SEO techniques, while foundational, often fall short in the swiftly changing search landscape. This paper presents a cutting-edge solution that leverages the power of deep learning and generative AI to redefine image SEO. Through the innovative integration of the AWS Rekognition's image analysis and ChatGPT's advanced natural language processing, we automate and refine the creation of alt-texts, ensuring they are not only precise, but also SEO-enriched. The result is a harmonious blend of technology and strategy that significantly boosts online content discoverability. Using a specific website as a test environment, the proposed methodology demonstrated a profound impact on SEO performance. Initially, no images on the webpage had alt-texts. After applying the AI-driven approach, all images were equipped with descriptive, SEO-optimized alt attributes, markedly improving their visibility in search engine results. These results underscore the effectiveness of integrating advanced AI technologies in SEO strategies, providing a scalable and effective framework for enhancing digital content accessibility and search engine rankings.

Metaverse and Fashion: An Analysis of Consumer Online Interest

Recent studies have demonstrated the value that the Internet and web applications bring to businesses. Among other tools are those that enable the analysis and monitoring of searches, such as Google Trends, which is currently used by the fashion industry to guide experiential practices in a context of augmented reality and/or virtual reality, and even to predict purchasing behaviours through the metaverse. Data from this tool provide insight into fashion consumer search patterns. Understanding and managing this digital tool is an essential factor in rethinking businesses’ marketing strategies. The aim of this study is to analyse online user search behaviour by analysing and monitoring the terms “metaverse” and “fashion” on Google Trends. A quantitative descriptive cross-sectional method was employed. The results show that there is growing consumer interest in both concepts on the Internet, despite the lack of homogeneity in the behaviour of the five Google search tools.

Predicting changes in task difficulty perception based on visual behavior in mobile health information search

With the proliferation of online health resources, mobile health information search has become the new norm, in which the task difficulty perception in search affects the user's search experience. This study aimed to investigate the relationship between visual behavior that reflects users' cognitive processing and changes in perceived task difficulty, thereby predicting such changes.This study was conducted through a controlled experiment. 46 participants were recruited to complete four tasks. Visual behavior data were collected through eye-tracking technology, and changes in task difficulty perception were measured through pre-task and post-task questionnaires. The mobile health information search process is divided into three search activities: querying, browsing, and viewing activities. Predictors were inspected from the overall session and individual search activity levels using the Mann-Whitney U test, and then K-Nearest Neighbor, Extreme-Trees, Naive Bayesian, Support Vector Machine, Logistic Regression algorithms were used to predict and evaluate prediction effects.The results showed significant differences in participants' fixation and saccade behaviors between increases and decreases in task difficulty, both at the overall session and individual search activity level. The logistic regression algorithm demonstrated the highest predictive performance, Furthermore, visual behavioral indicators for the browsing activity proved to perform better relative to the other search activities.This study highlights the importance of visual behavioral indicators as reliable predictors of changes in users' perceived task difficulty in mobile health information search. It can help health information providers and administrators to provide timely and targeted assistance and implement effective guidance strategies.

How different text display patterns affect cybersickness in augmented reality.

Cybersickness remains a pivotal factor that impacts user experience in Augmented Reality (AR). Research probing into the relationship between AR reading tasks and cybersickness, particularly focusing on text display patterns and user characteristics, has been scant. Moreover, the influence of cybersickness on searching ability and the broader spectrum of user experience has not been rigorously tested. Recent investigations have aimed to pinpoint the variables that contribute to cybersickness during AR reading sessions. In one such study, 40 participants underwent a series of controlled experiments with randomized text display patterns, including variations in text speed and text movement modes. Post-experiment, participants completed a questionnaire that helped quantify their experiences and the degree of cybersickness encountered. The data highlighted that satiety, text speed, and text movement mode are significant contributors to cybersickness. When participants experienced higher levels of cybersickness, font color stood out as a particularly influential factor, whereas gender differences seemed to affect the onset of cybersickness more noticeably at lower levels. This study also drew attention to the impact of cybersickness on search ability within AR environments. It was noted that as cybersickness intensity increased, search ability was markedly compromised. In sum, the research underscores the importance of text display patterns and user characteristics, such as past AR experience, in understanding cybersickness and its detrimental effects on user experience and search ability, particularly under conditions of intense cybersickness.

Movie recommendation based on ALS collaborative filtering recommendation algorithm with deep learning model

Development of recommender systems has recently emerged as a prominent study field that has drawn the attention of several scientists and researchers worldwide. Various fields, such as music, movies, books, news, search queries, and commercial goods, employ recommender systems. One of the well-liked and effective RS strategies is the collaborative filtering algorithm, which seeks out users who are quite similar to active one to propose products. This study suggests a unique method for recommending films based on analysis of user preference data that combines ALS collaborative filtering with deep learning techniques. The input in this case is gathered as web data based on previously performed user searches and then processed for noise reduction and normalisation. Convolutional multimodal auto multilayer graph with ALS collaborative filtering (CMAMG_ALSCF) was used to classify this processed data according to user evaluations and interests. Movies that are related to the interests of users are recommended by examining the similarity between users and other users or the similarity between movies and other movies. For several movie recommendation datasets, experimental analysis is done in terms of training accuracy, validation accuracy, RMSE, and average precision.

A Survey of On Personalized E-Commerce

The Personalized e-commerce application proposed by us incorporates several essential modules to provide a seamless and convenient shopping experience for users. The app utilizes GPS or network-based location services to determine the user's location and search for nearby shops within a specified radius of 5 km. The app also allows users to set preferences such as cuisine, product category, and price range, which are used to filter the list of nearby shops and display only those that match the user's preferences. The app provides relevant information for each shop, such as its name, address, product offerings, prices, and rating options to get directions to the selected shop. Additionally, the app will enable users to make purchases either by redirecting them to the shop's website or by implementing an in-app purchasing process. With these features, the proposed personalized e-commerce app offers a personalized and convenient shopping experience for users, allowing them to find and purchase products from nearby shops with ease.

Products Reviews and Sentimental Analysis System for Ecommerce Website

Thought mining is a widely used topic in today's world; The internet contains a lot of valuable information used by different companies for different purposes. Our goal is to create a web application with machine learning models that can identify user reviewsof specific products. It shows the advantages and disadvantages of checking reviews of products that are useful to users. In this application, when the user searches for a product, comment data is collected from the e-commerce site and transferred to the machine learning model, which is the Naive Bayes tool, to enable positive and negative emotions to be identified separately according to the extracted features. . from themodel. We show users all the positive and negative polarities of the reviews for the products they are looking for, and we also clearly show how we arrived at the results. Therefore, these results can help users make decisions about products..

Development of a multimedia training course for user self-development

The object of this study is the composition, content, and features of analytical and applied tasks that must be solved in the process of creating a multimedia training course for user self-development. In the context of this work, the problem of the lack of a clear definition of the content-element composition of analytical and applied tasks, which must be solved during the creation of such a course, was solved. An analysis of research into various aspects of the creation of multimedia publications, complexes, courses for self-education and self-development of users in various fields is provided. The systematization and determination of the content and features of analytical and applied tasks were carried out, which should be resolved during the development of the course. Keyword research is based on the analysis of user searches. As a result, "succulent species" (1–10k/month) and "breeding succulents" (100–1k/month) were added as course topics. In order to identify positive practices and bring them to the course, the functionality of analogs was investigated. The development of the technical task makes it possible to determine the elemental and content composition, structural and technological aspects of the course. The choice of the most appropriate software environment for the development of a multimedia educational course was substantiated (through the prism of the analysis of the functionality required for this purpose). An appropriate stylistic direction for the implementation of the design of the course interface has been determined. Page prototypes were created. The implementation of the course and its interactive components (advice, exercises, tests, games) was carried out in the selected Adobe Captivate environment. The solution of each task is illustrated by an example. To demonstrate the creation of a multimedia course, self-development of the user in matters of caring for succulents was chosen. The developed publication is used in the educational discipline "Technologies of electronic publishing" as an illustrative example of creating a course.

The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model

Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.

Assessing knowledge organization systems from a gender perspective: Wikipedia taxonomy and Wikidata ontologies

PurposeDevelop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.Design/methodology/approachThis study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.FindingsThis study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.Originality/valueThe assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

Impact of Digital Marketing Post Covid-19

Digital marketing has grown rapidly since the 1990s. First it was the basic website and email. In the late 2000s, social media platforms emerged that allowed brands to communicate directly with their target audience. The advent of Google AdWords enabled targeted ads based on user searches. In the 2010s, smartphones created pockets of mobile marketing. Content marketing, which focuses on important information to attract customers, has grown in popularity. The video market has grown with platforms like YouTube. The market of influencers emerged, increasing the popularity of individuals. In mid-2010, he saw the power of advertising in social networks, which uses user data for the right purpose. Automation and AI have become essential for personalized experiences. Chatbots have revolutionized customer interactions. Native ads are well integrated with content. Regulations like GDPR have become stricter due to privacy concerns. The 2020s saw the introduction of immersive technologies such as AR and VR for a better customer experience. Refine campaigns with data analytics and AI insights. Today, digital marketing spans a variety of channels, from social media and content to SEO, email, and more. It is a dynamic environment created by changes in technology, consumer behavior and legislation, which will continue to provide important opportunities to connect businesses with their audiences.

E-commerce Webpage Recommendation Scheme Base on Semantic Mining and Neural Networks

In e-commerce websites, web mining web page recommendation technology has been widely used. However, recommendation solutions often cannot meet the actual application needs of online shopping users. To address this problem, this paper proposes an e-commerce web page recommendation solution that combines semantic web mining and BP neural networks. First, the web logs of user searches are processed and 5 features are extracted: content priority, time consumption priority, online shopping users' explicit/implicit feedback on the website, recommendation semantics and input deviation amount. Then, these features are used as input features of the BP neural network to classify and identify the priority of the final output web page. Finally, the web pages are sorted according to priority and recommended to users. This project uses book sales webpages as samples for experiments. The results show that this solution can quickly and accurately identify the webpages required by users.