User Search Research Articles

From linear to non-linear: investigating the effects of right-rail results on complex SERPs

Modern search engine result pages (SERPs) become increasingly complex with heterogeneous information aggregated from various sources. In many cases, these SERPs also display results in the right rail besides the traditional left-rail result lists, which change the linear result list to a non-linear panel and might influence user search behavior patterns. While user behavior on the traditional ranked result list has been well studied in existing works, it still lacks a thorough investigation of the effects caused by the right-rail results, especially on complex SERPs. To shed light on this research question, we conducted a user study, which collected participants’ eye movements, detailed interaction behavioral logs, and feedback information. Based on the collected data, we analyze the influence of right-rail results on users’ examination patterns, search behavior, perceived workload, and satisfaction. We further construct a user model to predict users’ examination behavior on non-linear SERPs. Our work contributes to understanding the effects of the right-rail results on users’ interaction patterns, benefiting other related research, such as the evaluation and UI optimization of search systems.

Developing a Meta-Suggestion Engine for Search Queries

Typically, search engines provide query suggestions to assist users in the search process. Query suggestions are very important for improving users search experience. However, most query suggestions are based on the user's search logs, and they can be influenced by infrequently searched queries. Depending on the user's query, query suggestions can be ineffective in global search engines but effective in a domestic search engine. Conversely, it can be effective in global engines and weak in domestic engines. In addition, log-based query suggestions require many search logs, which makes them difficult to construct outside of a large search engine. Some search engines do not provide query suggestions, making searches difficult for users. These query suggestion vulnerabilities degrade the user's search experience. In this study, we develop a meta-suggestion, a new query suggestion scheme. Similar to meta-searches, meta-suggestions retrieves candidate queries of suggestions from other search engines. Meta-suggestions generate suggestions by reranking the aggregated candidate queries. We develop a meta-suggestion engine (MSE) browser extension that generates meta-suggestions. It can provide query suggestions for any webpage and does not require a search log. Comparing our meta-suggestions to major search engines such as Google, showed a 17% performance improvement on normalized discounted cumulative gain (NDCG) and a 31% improvement on precision. If more detailed factors, such as user preferences are discovered through continued research, it is expected that user searches will greatly improve. An enhanced user search experience is possible if factors, such as user preference, are examined in future work.

Evaluation of Prototype CE-MS Interfaces.

Capillary electrophoresis-mass spectrometry (CE-MS) coupling is a powerful analytical solution bringing together the separation power of CE and the wealth of chemical information afforded by MS. Nevertheless, interfaces making the hyphenation of both techniques possible have always been the subject of a quest for improvement by their users in search for more sensitive and robust setups. This fact has led to numerous technical developments and new interface designs claiming to outrival existing approaches in different aspects. Nevertheless, the task of evaluating and comparing a new interface to previous solutions is not always straightforward. Issued from our own experience in the field, we herein propose a protocol to optimize the operation parameters of a new CE-MS interface design, assess its analytical performance, and compare it to a reference interface if desired. Electrospray stability, sensitivity, reproducibility, and robustness are practically evaluated as key elements of the process.

Information retrieval and data mining through library OPAC keyword searching: An experience of recent trends and JNU, New Delhi, Library

This paper aims to explain Library Web OPAC keyword searching and data mining techniques. Data mining techniques recommend digital library services based on the user's profile and search history. This paper also discusses the web OPAC service and keyword searching technique. Moreover, the paper provides a brief description of the various search option facilities provided by the Koha Web OPAC, a service at Dr. BR Ambedkar Central Library, JNU, New Delhi and highlights the recent trend that reflects increasing use of various software applications in libraries to interact with library collections and searching the pin-pointed information.

Разработка системы навигации и поиска карт для онлайн-версии Экологического атласа России

An online version of the Ecological Atlas of Russia, published in 2017, is currently being developed. The article sets the task of cataloging atlas maps satellite images, which is necessary to provide user navigation, search and compilation of thematic collections. The article analyzes a number of Russian and foreign online atlases and their navigation systems, on the basis of which a system of cataloging and classification of maps for the electronic version of the Ecological Atlas of Russia has been developed. The cataloging of maps satellite images is based on a system of parameters that are assigned to each map. These include the identification number of the image, page numbers in the paper version of the atlas, the name of the section, territorial coverage, scale, text of the legend and some other parameters. Special attention during cataloging is paid to assigning keywords and phrases to all maps and satellite images. Keyword search will give the user the opportunity to compile collections of thematically similar images, even if they are located in different sections of the atlas. A prototype of an interface designed to search for maps has also been developed. The search can be carried out by several parameters at the discretion of the user. The search result will be a list of images with previews, titles and links. When opening the map, for the convenience of the user, links will also be provided to the text description of the section to which this map belongs, to thematically close maps, as well as to previously viewed maps. The principles of cataloging and searching maps proposed in this study can be used in the development of online versions of other complex thematic atlases.

ОНТОЛОГИЧЕСКАЯ МОДЕЛЬ ОБРАЗОВАТЕЛЬНОГО ПОРТАЛА ВУЗА

The article discusses the main aspects of building the ontology of the educational portal of the university from design to development. Examples of working with Protégé 4.3 and Sparql queries are given. In addition, the author talks about the importance of working on an educational portal in terms of developing the semantic core and the relevance of the portal for search engines. The author proposes a methodology for using Semantic Web technologies to form the structure and content of the university's educational portal. Develop an ontology of the educational portal to provide the possibility of semantic analysis of user search queries in order to organize a rational structure and content of the educational portal of the university and ensure the possibility of effective promotion of the portal in the Internet environment. In the ontological model, the author considers in the form of classes a set of educational programs of the university, and their constituent educational courses, as well as user profiles. Relations between classes will further help, when implementing user information requests, to determine the degree of compliance of each educational program (field of study) with the needs and preferences of a particular user, and to implement automatic selection of university educational programs recommended for applicants. An ontological model of the educational portal has been developed. The ontological model allows you to optimally structure the content of the educational portal, which further provides the possibility of issuing recommendations to users regarding the choice of the direction of training (applicants) or educational resources (students or teachers) in accordance with their needs and preferences. The developed ontology is used both at the stage of developing the educational portal of the university and its semantic core, and at the stage of functioning of the educational resource, as a knowledge base in the system for issuing recommendations to users on choosing the direction of training.

Construção da integralidade na Rede de Atenção às Urgências e Emergências: o cuidado para além dos serviços

This research aims to analyze the dimensions of care and health needs reported by users of the Urgent and Emergency Care Network (RUE) in a health region. The qualitative study was conducted by collecting statements from health service users about their experiences with the illness process and the use of RUE. The analysis considered the references of the “Taxonomy of Health Needs” and the “Multiple Dimensions of Care Management”. The statements emphasized the users search for autonomy and the action of family care. Understanding the health needs of users and the different dimensions of care management is essential to formulate health policies and organize networks towards comprehensiveness.

Correlating <i>pre-search</i> and <i>in-search</i> context to predict search intent for exploratory search

Modern information systems are expected to respond to a wide variety of information needs from users with diverse goals. The topical dimension ('what' the user is searching for) of these information needs is well studied; however, the intent dimension ('why' the user is searching) has received relatively less attention. Traditionally, the intent is an immediate reason, purpose, or goal that motivates the user search, and captured in search contexts (pre-search, in-search, pro-search). An ideal information system would be able to use. This article proposed a novel intent estimation strategy, based on the intuition that captured intent proactively extracts potential results. Captured pre-search context adapts query term proximities within matched results beside document-terms statistics and pseudo-relevance feedback with user-relevance feedback for in-search. The assessment asserts the superior performance of the proposed strategy over equivalent on trade-offs, e.g., novelty, diversity (coverage, topicality), retrieval (precision, recall, F-measure) and exploitation vs. exploration.

How Reliable and Popular are Trabeculectomy Videos on Youtube?

Evaluating the quality, dependability, and popularity of YouTube videos about trabeculectomy. A simulated user search for trabeculectomy videos on YouTube was conducted using the keywords "trabeculectomy, trabeculectomy surgery for glaucoma, and trabeculectomy surgery." Hundred out of the one hundred and fifty videos met the criteria and were analyzed. To assess quality and reliability, each video was evaluated by two independent reviewers using the DISCERN (scale, 1-5), Journal of the American Medical Association (JAMA; scale, 0-4), and Global Quality (GQ; scale, 1-5) criteria. The popularity of the videos was evaluated by Video Power Index (VPI). Videos were further classified into three groups based on the source of their upload. Of the 100 analyzed videos, 50 were uploaded to the system by doctors, 40 by health institutions and 10 by patients. Fifty-seven percent are videos with surgical content. The mean DISCERN score was 44.84 ± 8.14 the mean JAMA score was 2.08 ± 0.67, and the mean Global Quality score was 2.02 ± 0.72. Although some videos provided adequate information, the majority of the videos were rated as fair. While the DISCERN, JAMA, GQS scores were statistically higher in videos uploaded by doctors than in videos uploaded by patients (P < 0.01), VPI was higher in videos uploaded by patients (P = 0.003). Nonsurgical videos had the highest rate of likes and comments (P < 0.05). No substantial difference in scoring was observed between the 2 independent reviewers (P < 0.05). Videos with high popularity had low information quality and reliability. This situation presupposes video sharing in a more understandable language for patients.

An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web Content

The recent advancements in the web allow users to generate multimedia content, resulting in multimedia information proliferation. Existing search engines provide access to multimedia content via a disjoint assembly of media-specific results called verticals. However, this decentralized assembly of media contents requires manual aggregation and synthesizing efforts at the user&#x2019;s end, hindering the information exploration process and subsequently may cause cognitive overload, hence, demanding innovative tools to discover multimedia content. The researchers have devised numerous state-of-the-art approaches; however, analysis to confirm the efficacy has little emphasis. This study investigates users&#x2019; complex multimedia information-seeking behavior over state-of-the-art web search systems to unveil the user&#x2019;s information-seeking issues. Our research employs between-subjects study and post hoc analysis strategies to analyze participants&#x2019; information-seeking characteristics. The study design adopted statistical hypothesis testing to consolidate previous user behavioral studies, confirm existing strategies, and present recommended practices for future general-purpose web search engines. The participants were assigned Google and an advanced discovery search system using the same multimedia dataset to ensure the obtained results&#x2019; credibility. The primary behavioral parameters include search efforts, multimedia content exploration, search user interface (SUI), information management and presentation, and user cognition. This study uncovers several inadequacies of the search engines in meeting users&#x2019; complex discovery needs, including 29.6% less user engagement, 43% system and searching dissatisfaction, and 32% less knowledge acquisition with 63.9% increased clicking effort on traditional search engines. The results confirmed previous user studies and suggest novel research recommendations statistically significant in multimedia information exploration-related endeavors.

Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization

In this article, we propose a Latent Dirichlet Allocation– (LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user’s search intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user’s search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user’s search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models.

Time Classification Algorithm Based on Windowed-Color Histogram Matching

A web-based search system recommends and gives results such as customized image or video contents using information such as user interests, search time, and place. Time information extracted from images can be used as a important metadata in the web search system. We present an efficient algorithm to classify time period into day, dawn, and night when the input is a single image with a sky region. We employ the Mask R-CNN to extract a sky region. Based on the extracted sky region, reference color histograms are generated, which can be considered as the ground-truth. To compare the histograms effectively, we design the windowed-color histograms (for RGB bands) to compare each time period from the sky region of the reference data with one of the input images. Also, we use a weighting approach to reflect a more separable feature on the windowed-color histogram. With the proposed windowed-color histogram, we verify about 91% of the recognition accuracy in the test data. Compared with the existing deep neural network models, we verify that the proposed algorithm achieves better performance in the test dataset.

Characterizing the Influence of Confirmation Bias on Web Search Behavior.

In this study, we analyzed the relationship between confirmation bias, which causes people to preferentially view information that supports their opinions and beliefs, and web search behavior. In an online user study, we controlled confirmation bias by presenting prior information to participants that manipulated their impressions of health search topics and analyzed their behavioral logs during web search tasks. We found that web search users with poor health literacy and negative prior beliefs about the health search topic did not spend time examining the list of web search results, and these users demonstrated bias in webpage selection. In contrast, web search users with high health literacy and negative prior beliefs about the search topic spent more time examining the list of web search results. In addition, these users attempted to browse webpages that present different opinions. No significant difference in web search behavior was observed between users with positive prior beliefs about the search topic and those with neutral belief.

APLIKASI GAME EDUKASI BELAJAR MENGHAFAL AYAT-AYAT PENDEK BERBASIS ANDROID MENGGUNAKAN METODE INTERPOLATION SEARCH (STUDI KASUS : TK SULAMUL ULUM)

This research produces output in the form of android-based educational games that can be used in early childhood learning. The results of testing on students illustrate that games can motivate students in learning, especially the introduction of short verses. The interpolation search method helps users search for short verses to be learned.

Dynamic targets searching assistance based on virtual camera priority

BackgroundWhen a user walks freely in an unknown virtual scene and searches for multiple dynamic targets, the lack of a comprehensive understanding of the environment may have a negative impact on the execution of virtual reality tasks. Previous studies can help users with auxiliary tools, such as top view maps or trails, and exploration guidance, for example, automatically generated paths according to the user location and important static spots in virtual scenes. However, in some virtual reality applications, when the scene has complex occlusions, and the user cannot obtain any real-time position information of the dynamic target, the above assistance cannot help the user complete the task more effectively. MethodsWe design a virtual camera priority-based assistance to help the user search dynamic targets efficiently. Instead of forcing users to go to destinations, we provide an optimized instant path to guide them to places where they are more likely to find dynamic targets when they ask for help. We assume that a certain number of virtual cameras are fixed in virtual scenes to obtain extra depth maps, which capture the depth information of the scene and the locations of the dynamic targets. Our method automatically analyzes the priority of these virtual cameras, chooses the destination, and generates an instant path to assist the user in finding the dynamic targets. Our method is suitable for various virtual reality applications that do not require manual supervision or input. ResultsA user study is designed to evaluate the proposed method. The results indicate that compared with three conventional navigation methods, such as the top-view method, our method can help users find dynamic targets more efficiently. The advantages include reducing the task completion time, reducing the number of resets, increasing the average distance between resets, and reducing user task load. ConclusionsWe presented a method for improving dynamic target searching efficiency in virtual scenes by virtual camera priority-based path guidance. Compared with three conventional navigation methods, such as the top-view method, this method can help users find dynamic targets more effectively.

Improving search and recommendation by asking clarifying questions

Recently, there has been renewed interest in interactive search and recommender systems through the development of systems that ask clarifying questions (CQs) to users to better understand their information needs. Although the significance and effectiveness of CQs have been recongnized, research on modelling CQs to improve search and recommender systems is limited, compared to traditional search and recommender systems. Also, understanding users' willingness or ability to answer these CQs, and the extent to which CQs impact users' behavior and their ability to identify relevant information remains relatively unexplored. The work in this thesis provides a series of models and online user studies for search and recommender systems that construct and use CQs. In the first part of the thesis, we focus on CQ-based search and recommendation algorithms. We first propose a novel CQ-based document search algorithm, Sequential Bayesian Search based method for Technology-Assisted Review (SBSTAR) and its extension, to efficiently retrieve the last few, but significant, relevant documents [Zou et al., 2018; Zou and Kanoulas, 2020]. The algorithm sequentially selects and directly asks CQs to users about the presence or absence of an entity in the missing relevant documents. Next, we propose a novel CQ-based product search algorithm, Question-based Sequential Bayesian Product Search (QSBPS), to effectively locate products that users are looking for [Zou and Kanoulas, 2019]. The model is based on a duet learning framework that learns product relevance and the reward of the potential CQ to be asked to the user. Furthermore, we propose a novel CQ-based recommendation algorithm, Question-based recommendation (Qrec), to assist users to find items interactively [Zou et al., 2020a]. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online based on the user's answers. We experimentally test the performance of the proposed models and demonstrate that they outperform state-of-the-art baselines. In the second part of the thesis, we focus on the evaluation of CQ-based search and recommendation. We first conduct an online user study by deploying a CQ-based system in the domain of online retail, to understand to what extent users can answer CQs. We explore the user willingness, ability, and user perception to provide answers to CQs [Zou et al., 2020b]. Next, we conduct a large user study on web search to understand the impact of CQ interactions on user search behavior and satisfaction, and how users interact with different levels of quality of CQs under different user perceptions and conditions. Awarded by : University of Amsterdam, Amsterdam, The Netherlands on 6 September 2021. Supervised by : Evangelos Kanoulas, Maarten de Rijke. Available at : https://hdl.handle.net/11245.1/3c46aa78-98af-4997-aef7-44db7ab2fa3d.

Large-scale interactive retrieval in art collections using multi-style feature aggregation.

Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many research opportunities. However, manual approaches are inadequate to handle this amount of data, and it requires appropriate computer-based methods to analyze them. This article presents a visual search algorithm and user interface to support art historians to find objects and motifs in extensive datasets. Artistic image collections are subject to significant domain shifts induced by large variations in styles, artistic media, and materials. This poses new challenges to most computer vision models which are trained on photographs. To alleviate this problem, we introduce a multi-style feature aggregation that projects images into the same distribution, leading to more accurate and style-invariant search results. Our retrieval system is based on a voting procedure combined with fast nearest-neighbor search and enables finding and localizing motifs within an extensive image collection in seconds. The presented approach significantly improves the state-of-the-art in terms of accuracy and search time on various datasets and applies to large and inhomogeneous collections. In addition to the search algorithm, we introduce a user interface that allows art historians to apply our algorithm in practice. The interface enables users to search for single regions, multiple regions regarding different connection types and holds an interactive feedback system to improve retrieval results further. With our methodological contribution and easy-to-use user interface, this work manifests further progress towards a computer-based analysis of visual art.

Anomaly Detection for the Centralised Elasticsearch Service at CERN.

For several years CERN has been offering a centralised service for Elasticsearch, a popular distributed system for search and analytics of user provided data. The service offered by CERN IT is better described as a service of services, delivering centrally managed and maintained Elasticsearch instances to CERN users who have a justified need for it. This dynamic infrastructure currently consists of about 30 distinct and independent Elasticsearch installations, in the following referred to as Elasticsearch clusters, some of which are shared between different user communities. The service is used by several hundred users mainly for logs and service analytics. Due to its size and complexity, the installation produces a huge amount of internal monitoring data which can be difficult to process in real time with limited available person power. Early on, an idea was therefore born to process this data automatically, aiming to extract anomalies and possible issues building up in real time, allowing the experts to address them before they start to cause an issue for the users of the service. Both deep learning and traditional methods have been applied to analyse the data in order to achieve this goal. This resulted in the current deployment of an anomaly detection system based on a one layer multi dimensional LSTM neural network, coupled with applying a simple moving average to the data to validate the results. This paper will describe which methods were investigated and give an overview of the current system, including data retrieval, data pre-processing and analysis. In addition, reports on experiences gained when applying the system to actual data will be provided. Finally, weaknesses of the current system will be briefly discussed, and ideas for future system improvements will be sketched out.

A product recommendation system for solving the cold start problem

The recommendation of product or service based on application needs different product attributes and user requirements to be analyzed. But if both the kinds of information are unavailable then such kind of issue in the recommendation system is known as the clod start problem.[1] In this presented work, the effort is made in order to understand and resolve the issue of recommendation system. Thus, a four phase recommendation system is introduced in this work. In first phase, the web usage data is preprocessed which is used for further recommendations of products. In next phase, the frequent pattern based recommendation is performed. In third phase, the recommendation is made on the basis of user current search (user click streams) and similar user behaviors available in web access logs. Additionally, the final recommendation is made on the basis of filtering the results obtained in third phase and the cost, brand and social review of the product or service. The implementation of the system is performed on JAVA technology and the results in terms of accuracy, error rate is computed. The results show the prediction is accurate and required less computational resources. Thus, in recommendation system design the model is acceptable for use and future extension of work for solving the issue of cold start problem.

Examining the Correlation of Google Influenza Trend with Hospital Data: Retrospective Study.

IntroductionMany studies have explored social media and users search activities such as Google Trends to predict and detect influenza activities. Studies that examined Google Trends correlation with the actual hospital influenza cases were conducted in non-tropical regions that have clearly defined seasons. Tropical areas are known for having less-defined seasonality and the extent of Google Trends concordance with actual influenza cases is unknown for these areas. The goal of this study is to compare Google Trends with hospital cases in tropical regions.MethodsWe analyzed 48,263 influenza cases in the time period of 2010 to 2019. The cases were retrieved from central hospital medical records in tropical regions using the corresponding codes for influenza ICD-10 AM. Cases from the medical records were compared with Google Trends to determine trends, seasonality, and correlation.ResultsGraphically, there were some similar areas of the trend, but cross-correlation analysis did not show any significant correlation between hospital and Google Trends with a maximum correlation rate of 0.300. Seasonality analysis showed a clear pattern that peaked around November in Google Trends while hospital data showed less defined seasonality with a smaller peak occurring at the end of December and beginning of January.ConclusionBased on the results, there is a weak correlation between Google Trends and hospital data. More innovative methods are emerging to predict influenza activity using social media and user search data and further study is needed to examine the concurrent trends derived using these methods across regions that have different humidity levels and temperatures.