User Search Research Articles

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval.

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates - and to do so with enough efficiency at large scale. We propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

A new Query Reformulation Approach using Web Result Clustering and User Profile

Query reformulation techniques are essential for information retrieval systems. These techniques eliminate the bad queries (short and ambiguous queries) and minimize users search time. There are two kinds of techniques in literature; the first uses the query logs with the click-through data to identify relevant queries and propose them to the user. The second group uses relevant terms extracted from different sources such as Wikipedia, Wordnet, and pseudo-relevant documents to expand the initial query and increase the likelihood with relevant documents. Both groups generate false queries because they do not consider the user interests and due to the assumption that the user clicks only on relevant results (first group) and that the top-k retrieved documents (top 5 to 15) are relevant (second group).In this paper, we propose a novel approach to reformulate the user queries using his profile (which contains the user interests). The approach retrieves documents related to the query from four different data sources (≈ 1000 doc). Then, it extracts the topics of these documents using the Lingo clustering algorithm (cluster the document with the same topic) and Text razor API (extract the potential topics of each cluster). Finally, it generates queries based on these topics and the user profile. The results show that the proposed approach outperforms the existing solution in P@10, P@20, and MAP.

Improved location filtering using a context-aware approach

With the pervasiveness of GPS-enabled devices, a considerable number of GPS traces are accumulating continuously and unobtrusively in online communities. However, almost all current applications directly use raw GPS data, such as coordinates and time stamps, without interpreting these data. Thus far, online communities cannot offer much support to users in terms of recommending geospatial locations. Furthermore, because the data sets involved are large, users cannot browse each GPS trajectory individually. Therefore, users’ GPS trajectories must be mined and then classified as positive or negative. When the number of ratings for a place exceeds a certain threshold, the place is considered suitable for the user. By contrast, when the ratings for a place are mostly negative, this place is considered unsuitable for the user. When a user searches for the best place, the recommender system determines the user’s location (latitude, longitude) and then sends the best-rated destinations and the shortest routes between the user’s location and the destination to the user’s mobile device. Experiments were conducted in this study to determine the requisite similarity for GPS data points, the user’s information, and the best route for the user.

Application of convolution neural network in web query session mining for personalised web search

In this paper, a deep learning convolution neural network (CNN) is applied in web query session mining for effective personalised web search. The CNN extracts high-level continuous clicked document/query concept vector for semantic clustering of documents. The CNN model is trained to generate document/query concept vector based on clickthrough web query session data. Training of CNN is done using backpropagation based on stochastic gradient descent maximising the likelihood of relevant document given a user search query. During web search, search query concept vector is generated and compared with semantic clusters means to select the most similar cluster for web document recommendations. The experimental results were analysed based on average precision of search results and loss function computed during training of CNN. The improvement in precision of search results as well as decrease in loss value proves CNN to be effective in capturing semantics of web user query sessions for effective information retrieval.

A google trends analysis: evaluation of the search frequency about alternative treatment methods during the COVID-19 pandemic in Turkey

Objective: A rapidly grown up information about COVID-19 has been regularly broadcast on internet websites or social media. In this period, it can be argued that there has been an increase in the online search about the COVID-19 infection, disease prevention or treatment methods. We investigated the Google research trends in searches of alternative treatment methods during the COVID-19 pandemic in Turkey. Methods: The Google TrendsTM (GTTM) was used to retrieve data on Internet user search activities and patterns of Google search queries. We searched most known keywords related to alternative and herbal therapies such as thyme, sumac, ginger, tonics, propolis, Vitamin D, Vitamin C and Zinc in GTTM between January 1, 2020 and May 5, 2020 in Turkish. Results: According to the GTTM analysis of Turkey as of the indicated dates, there have been an intensive search for COVID-19 and treatment methods in March. It was determined that the searches related to the molecules mentioned above peaked at different times and then the search interest decreased. It was also found that the level of social interest decreased with the prolongation of the pandemic period about COVID-19. Conclusion: The data we obtained as a result of our study demonstrated that there was an increase in the search on medicinal herbs and drugs by the internet users in Turkey during the COVID-19 pandemic.

BIB-MKS: Post-Quantum Secure Biometric Identity-Based Multi-Keyword Search over Encrypted Data in Cloud Storage Systems

Cloud computing technologies rely on powerful storage services to maintain massive data for users. Sensitive data are encrypted before outsourcing, but this limits the availability of data. Public-key encryption with keyword search (PEKS) contributes to searching target encrypted data with keywords. However, existing PEKS mechanisms require to manage certificates, they are also vulnerable to adversaries equipped with quantum-computing devices. In this paper, we devise a biometric identity-based multi-keyword search (BIB-MKS) mechanism from lattices over encrypted outsourced data, which inherently resists quantum-computing attacks. Each user in BIB-MKS is identified with her/his biometric information, which could be envisioned as the public key, thereby avoiding complex certificate managements. Particularly, BIB-MKS enables a data owner to produce an index associated with a biometric identity $BID'$ , such that a user with a biometric identity $BID$ issues multiple keywords in a single search query and retrieves corresponding encrypted data, if and only if $BID$ and $BID'$ are within a certain distance of each other as judged by some metric. BIB-MKS narrows down the search scope, and improves users search experience significantly. We define the formal security model of BIB-MKS, and prove the security of BIB-MKS under this model. The performance evaluation demonstrates that BIB-MKS is practical.

Can Web Search Queries Predict Prices Change on the Real Estate Market?

This study aims to explore whether the intensity of internet searches, according to the Google Trends search volume index (SVI), is a predictor of changes in real estate prices. The motivation of this study is the possibility to extend the understanding of the extra predictive power of Google search engine query volume of future housing price change (shift direction) by (i) the introduction of a research approach that combines the advantages of the complementary use of cross-correlation analysis and machine learning classification algorithms; (ii) applying the multi-class HPI values classifier which allows predicting the housing price increase, decrease or relative stability; (iii) exploiting the SVI that relates to interests in both ‘real estate’ and ‘credit to buy real estate’; (iv) evaluation of the introduced approach in the context of the Polish real estate market. The main theoretical contribution of our work is a confirmation that the freely available information regarding Google user searches can provide an in-depth insight into enriching the generally accepted statistics on supply and demand in the real estate market. From the practical perspective, this research confirms that SVI can be associated as a sole determinant to anticipate the housing price change with time-lag sufficient for making decisions regarding the purchase (sale) of individual property or the real estate market control. Such findings can be also helpful for researchers who intend to use Google Trends data as an extra variable from demand side to improve the prediction accuracy if it is included in the model which is based on the existing housing prices determinants.

OO-ABMS: Online/Offline-Aided Attribute-Based Multi-Keyword Search

Recently, Attribute-Based Encryption (ABE) is used as a baseline technology for keyword searching mechanisms to cater users’ security and privacy-related concerns who outsource their data to the powerful and resource-rich cloud server. Almost all the existing Attribute-Based Keyword Searching (ABKS) schemes either operate on a single-keyword or multiple; conjunctive-keyword search setting. These schemes work on the premise of an “all-or-nothing” retrieval mode, which greatly limits users’ searching capabilities. The most recent work incorporates multi-keyword ranked search in attribute-based encryption, which improves user search results accuracy and the flexibility of retrieval mode. However, these schemes demand high computation overhead, which ultimately suffers searching efficiency. This dilemma prevents further research and application of ABKS schemes, especially mobile-resource constrained devices such as mobile phones. This paper proposes an Online/Offline-aided Attribute-Based Multi-keyword Search (OOABMS) scheme to delegate most laborious computation operations to the offline phase before acquiring the attribute-based access control policy or keywords. An online phase then quickly assembles the pre-computed index or trapdoor with the required specifics when it becomes known. Theoretical comparison and simulation results show that our scheme is more efficient and practical in the real world scenario for mobile cloud computing in terms of computation overhead and flexibility.

SMART standards - concept for the automated transfer of standard contents into a machine-actionable form

Standards are - not directly visible to everyone – omnipresent in nearly every development process. In times of digitalization, where buzzwords such as "connectivity of machines", "artificial intelligence", “big data”, “cloud computing” or “smart factories” are often used, companies are still confronted with problems in handling standards throughout the entire product lifecycle. Today’s way of working with standards is characterized by manual viewing of documents, whereby a user searches for relevant information, such as formulas, and has to transfer this information to his process, method or tool. This manual process results in an increased time, loss of quality due to faulty manual transmission of information, a high adjustment effort for updates of standards and no guarantee for traceability. In order to reduce and minimize errors and needed time for work with information stored within standards, there is a need for a new form of knowledge representation for standards with sufficient data quality to ensure standard-compliant development activities. Consequently, there is a need for machine-actionable standards to ensure autonomous and efficient processes, whereby the effort for preparation is less than the benefit. The question arises how classified standards content can be represented in a machine-actionable way without loss of information. This paper shows a concept for the automatic extraction of standards content and their transfer into a machine-actionable knowledge representation. The concept, which is based on the “3M Framework of Duisburg” and thus answers questions of modularization, modeling and management, consists of six steps "extraction", "modeling", “modification”, "fusion and storage", "provision" and "application", to digitalize existing content, is presented and discussed.

REDUCE: a semi-supervised scalable approach for REsult DUplication detection in Search Engines

Search engines are among the most popular web services on the World Wide Web. They facilitate the process of finding information using a query-result mechanism. However, results returned by search engines contain a lot of duplications. For instance, when a user searches for a query q on Google, he will get n number of links divided into m number of pages. Although returned links have different content, exactly similar content is still existing on different links returned by search engines. This problem is referred to as duplication. Solving this problem, will increase the quality of search results as well as reducing the time search per query. In this paper, we introduce a new method called REDUCE (REsult DUplication detection in searCh Engines), to address this problem. It implements a semi-supervised approach. It approximately measures the similarity between the web pages and we suggest a new method to group the search results based on their similarity. To evaluate our method, we collect data from Google and other search engine platforms. We show that our method can solve this problem on different search engine platforms with different languages. We empirically evaluated our results on different classification algorithms and reached an accuracy of 96.7%.

Usability Evaluation of Web Search User Interfaces from the Elderly Perspective

The elderly population is increasing in many countries, often with health and incapacity challenges, largely disengaged them from the world of digital tools like Internet usage. They browse the Internet daily for obtaining needed information through various search engines through the search UI. Earlier technologies were fabricated for improving daily life, but the specific needs of the elderly are neglected often. Currently, available online search UIs are well-developed, but they did not consider usability in their design specifically for the elderly. This research aims to evaluate web search UIs based on the elderly perspectives to identify existing search UIs usability issues and recommend improvements to web search UI designs. The observation technique evaluated two web search UIs (Google interface and Bing interface) with fifteen participants aged 60 years and above. System Usability Scale (SUS) questionnaire was applied to measure the user satisfaction of the current two interfaces. The data collected from the observations were analyzed using content analysis, while the data acquired from the questionnaires were analyzed using the t-test. The results revealed a statistically significant difference in SUS ratings, with Google scoring 73.5 and Bing scoring 66.5, indicating that users prefer the Google interface over the Bing interface. Besides that, the usability issues were identified, and recommendations to improve the design of the search UI were suggested. These findings contribute to a better understanding of the issues that prevent elderly users from using web search UI and valuable feedback to designers on improving the UI to suit the elderly better.

Linear Elliptical Curve Digital Signature (LECDS) With Blockchain Approach for Enhanced Security on Cloud Server

Cloud computing is a continuously evolving technology that can enhance agility, availability, collaboration and scalability of data. Blockchain has a secure, immutable ledger which maintains all the transactions along with the timestamp. The blockchain framework and cloud computing technology jointly provides different ways of computational cost reduction. The existing methods help to identify the anonymous documents which are given in the form of requests from the cloud server. If the anonymized document requests are from the authorized users, then cloud provides better security and hence documents are not available for unauthorized users. But the main issue is access rights available for authorized users on sensitive data of the owner. To maintain the privacy the sensitive data are hidden using cryptographic techniques even for authorized users. The method adopted is Linear Elliptical Curve Digital Signature (LECDS) with Hyperledger blockchain, to prevent private data loss. The Linear regression method is used to classify the user information into two classes namely sensitive and non-sensitive. The non-sensitive data is encrypted using RSA and sensitive data is encrypted using LECC method. Modified Spider optimization search Algorithm (MSOA) is used to verify the integrity of outsourced data and search user query information in a cloud server. The hyper ledger blockchain verifies the user policy to create a private network through which the user communicates the cloud. In the analysis of the proposed method, the results are evaluated using various performance metrics such as security, throughput, classification accuracy and error rate.

How do multilingual users search? An investigation of query and result list language choices

AbstractMany users of search systems are multilingual, that is, they are proficient in two or more languages. In order to better understand and support the language preferences and behaviors of such multilingual users, this paper presents a series of five large‐scale studies that specifically elicit language choices regarding search queries and result lists. Overall, the results from the studies indicate that users frequently make use of different languages (i.e., not just their primary language), especially when they are provided with choices (e.g., when provided with a secondary language query or result list choice). In particular, when presented with a mixed‐language list choice, participants choose this option to an almost equal extent compared to primary‐language‐only lists. Important factors leading to language choices are user‐, task‐ and system‐related, including proficiency, task topic, and result layout. Moreover, participants' subjective reasons for making particular choices indicate that their primary language is considered more comfortable, that the secondary language often has more relevant and trustworthy results, and that mixed‐language lists provide a better overview. These results provide crucial insights into multilingual user preferences and behaviors, and may help in the design of systems that can better support the querying and result exploration of multilingual users.

Forecasting Russian Macroeconomic Indicators Based on Information from News and Search Queries

Modern economic literature features quite a number of various indices of economic activity. Some of them are based on public opinion polls (‘manual’ indices), while others are based on unstructured data from the Internet (‘automatic’ indices). However, the question as to which of these approaches is the most effective remains open. In this paper, we compare several different indices of economic activity in terms of their explanatory and predictive power. We build ‘automatic’ indices using machine learning methods. Search queries, news articles and user comments under news posts from social media are used as source data. The analysis of the resulting indices of economic activity shows that the search and news indices Granger-cause ‘manual’ indices and also better explain and predict the set of macroeconomic variables selected for research. The good explanatory power of the current values of macroeconomic indicators by means of current indices of economic activity with a lag in the output of macroeconomic statistics makes them suitable for nowcasting.

Chronotopic information interaction: integrating temporal and spatial structure for historical indexing and interactive search

Abstract Domain-based learning and research are important applications driving the development of exploratory search systems. A wealth of historical information about events from around the world resides within documents on the web, yet contemporary search engines do not take advantage of the closely integrated temporal and spatial information found within these web pages for indexing and design of search user interfaces. This gap limits the use of the web as a resource for historical and geohistorical information seeking. In this article, we propose chronotopic information interaction as a new interaction concept for web search that explicitly links temporal and spatial entities to keywords using a space–time grid index and a paired search user interface. The space–time grid index allows different modes of interaction between spatial, temporal, and keyword-based views in the search user interface. We demonstrate the use of the space–time grid index and chronotopic information interaction concept with the development of Pteraform, a prototype of a search engine that enables users to explore information in the English version of Wikipedia through a geohistorical lens.

Traceable and Controllable Encrypted Cloud Image Search in Multi-User Settings

With the advent of cloud computing, explosively increasing images are gradually outsourced to the cloud server for costs saving and feasibility. For security and privacy concerns, images (e.g., medical diagnosis, personal photos) should be encrypted before being outsourced. However, traditional encrypted image retrieval techniques still suffer from costly access control and low search accuracy. To solve these challenging issues, in this article, we first propose a Controllable encrypted cloud image Search scheme in Multi-user settings (namely CSM) by using the polynomial-based access strategy and proxy re-encryption technique. CSM achieves efficient access control and avoids heavy communication overhead caused by key transmission. Then, we improve the basic CSM to achieve malicious search user Tracing (namely TCSM) by utilizing the watermark technique, which can further prevent search users from illegally redistributing retrieved images to unauthorized search users. Our formal security analysis proves that our CSM (or TCSM) can guarantee the privacy of images, indexes, and search queries. Our empirical experiments using real-world datasets demonstrate the efficiency and high accuracy of our CSM (or TCSM) in practice.

Development of mobile application for Malay translated hadith search engine

This paper presents the development of mobile application for Malay Translated Hadith search engine. Limitations of current Hadith web application are the design is to optimize its usage on desktop computer but not on mobile devices, which requires simple and user friendly interface. Besides that, web application also needs internet connection to use. Due to increase usage of mobile application among mobile phone users, many existing web applications have moved to mobile based applications to cater for increasing numbers of mobile users. In this study, a mobile application for Android and iOS mobile application has been developed using Flutter framework, a hybrid mobile application framework. A Malay Translated hadith search engine mobile application can easily assist those who are seeking knowledge to learn more about certain topics in hadith, a second source of Islamic knowledge. This mobile application has search and directory features for users to browse the 2028 Sahih Bukhari hadith collection. Users can enter their query using search features to find selected hadith in Malay language. Queries will be processed for searching relevant hadith and display the results to the user. Evaluation using Recall and Precision shows that on the average Recall is 73% and Precision is 33%. Functionality testing is also conducted to test against the functional requirements or specifications. Results shows all requirements are successfully tested.

Метод разрешения лексической многозначности поискового запроса на основе онтологий

One of the factors influencing the relevance of search output is the multivalued search query, expressed by lexical means of a natural language. The multivalued lexical unit manifests itself at the stage of a search query. The method of removing the multivalence of lexical units in a search query based on ontologies is proposed. It is grounded that ontologies allow transferring semantic component of data related to a subject area accurately enough. The proposed method for lexical multivariance resolution can be described as follows. A user search query is received at the search engine input. The search engine contacts the ontology library to find the query. If the lexical unit from the search query is multivalued, the search engine will offer the user a list of subject areas in which the lexical unit from the search query was found. Oft the user searches in advance for the result from a particular subject area. When the subject area is defined, the search engine determines the nearest elements in the structure of ontology, and when ranking the search output will be guided by their presence or absence. The use of ontologies also allows adding synonyms and acronyms meaning the same to the search query. The proposed approach will allow solving lexical multiplicity and significantly relieving the search output, leaving only the subject area of interest to the user.

Facebook® como meio de divulgação científica: aliado ou inimigo?

Diferentes estratégias para divulgação de uma nova ferramenta de comunicação e interação em saúde, voltada para pessoas com deficiência, foram aplicadas no Facebook® com o objetivo de identificar quais estratégias de divulgação aplicadas ao Facebook® promoveram mais acessos à rede social D Eficiência e caracterizar seus usuários. Estudo quantitativo, descritivo e longitudinal, com dados extraídos do gerenciador de métricas do Facebook . A divulgação com busca ativa foi superior a passiva, sendo responsável por 91,4% (n=4.519) dos acessos, via celular (71%) por mulheres (81%). Publicações com hiperlink e imagens foram mais engajadoras. O Facebook® mostrou-se como uma ferramenta eficaz por atingir números expressivos de pessoas, e deve ser considerado na divulgação de novas tecnologias. Para tal, é necessário criar conteúdos atrativos e busca ativa de usuários. Estudos complementares devem continuar a avaliação dessa estratégia, com análise da divulgação orgânica e divulgação com impulsionamento pago.

When Are Search Completion Suggestions Problematic?

Problematic web search query completion suggestions-perceived as biased, offensive, or in some other way harmful-can reinforce existing stereotypes and misbeliefs, and even nudge users towards undesirable patterns of behavior. Locating such suggestions is difficult, not only due to the long-tailed nature of web search, but also due to differences in how people assess potential harms. Grounding our study in web search query logs, we explore when system-provided suggestions might be perceived as problematic through a series of crowd-experiments where we systematically manipulate: the search query fragments provided by users, possible user search intents, and the list of query completion suggestions. To examine why query suggestions might be perceived as problematic, we contrast them to an inventory of known types of problematic suggestions. We report our observations around differences in the prevalence of a) suggestions that are problematic on their own versus b) suggestions that are problematic for the query fragment provided by a user, for both common informational needs and in the presence of web search voids-topics searched by few to no users. Our experiments surface a rich array of scenarios where suggestions are considered problematic, including due to the context in which they were surfaced. Compounded by the elusive nature of many such scenarios, the prevalence of suggestions perceived as problematic only for certain user inputs, raises concerns about blind spots due to data annotation practices that may lead to some types of problematic suggestions being overlooked.