Mobile Search Research Articles

Application of E‐Commerce Interactive Marketing Model Based on Distributed Algorithm of Mobile Ad Hoc Network

With the development of the mobile Internet, e‐commerce has become one of the important ways of daily consumption, but how to effectively use e‐commerce for interactive marketing and increase sales is an important research direction. Mobile ad hoc distributed algorithms are introduced in this paper. Through sorting out the mode of e‐commerce interaction influence, process marketing is performed from two‐dimensional code, short message, business district, mobile search, Bluetooth, wireless network, and other methods, and interactive marketing is tried in various industries such as education, tourism, agriculture, catering, finance, and publishing, and simulation experiments are used to verify them. The simulation experiment results show that the mobile ad hoc distributed algorithms are effective and can support the e‐commerce interactive marketing model.

Artificial Intelligence for Enhanced Mobility and 5G Connectivity in UAV-Based Critical Missions

In the context of Fifth Generation mobile networks (5G), Search and Rescue (SAR) missions using Unmanned Aerial Vehicles (UAVs) can benefit from a dynamic, intelligent, and autonomous placement of both Network Functions (NFs) and Artificial Intelligence (AI) systems to quickly adapt in minimal human intervention scenarios. This article examines current 5G architectures and timely standardization efforts within this context. The contribution of this work is to identify associated 5G components and propose AI modules that enable efficient UAV-based SAR missions: the System Intelligence (SI) and Edge Intelligence (EI) concepts. SI is conceived as the entity responsible for defining and orchestrating the placement and processing tasks of NFs and AI systems, while EI is responsible for the optimization of AI-based end-user applications. The article also presents an open-source virtualized testbed that enables a concrete example of SI and EI roles in a SAR mission based on object detection with Deep Neural Networks (DNNs). In this proof-of-concept, the DNN layers are partitioned and the tradeoffs between communication and computational costs are highlighted. For instance, the results indicate that the latency can severely degrade the UAV trajectory and different DNN partitioning options can reduce the required bit rate to transmit DNN scores by more than three times.

ТЕНДЕНЦІЇ ТА ПРОГНОЗУВАННЯ РОЗВИТКУ ЕЛЕКТРОННОЇ ТОРГІВЛІ В УКРАЇНІ

The dynamics of e-commerce development indicators of Ukraine in hryvnia and dollar equivalent is analyzed. It is studied that the e-commerce market is represented by a large number of participants, among which the most popular are the sites of foreign companies: AliExpress, eBay, Amazon. However, with the creation of an increasingly active e-commerce space in Ukraine, the popularity of foreign platforms is declining, and domestic entities are increasingly using the experience of business models of other countries, implementing it in their own activities. Most of the e-commerce market in Ukraine is occupied by companies that use four business models: e-shop, e-bulletin board, e-marketplace and price aggregator; moreover, a significant number of companies use hybrid models such as «bulletin board + price aggregator», «supermarket + marketplace». Trends in the development of national e-commerce have been identified, in particular: growing consumer demand for personalized communications and reliability testing of e-commerce; expanding the use of contactless payment technologies, trade acquiring, augmented and visual reality; promotion of loyalty programs with embedded CRM-systems; integration of on-line and off-line sales channels in order to ensure maximum quality of customer service (omnichannel); attracting customers to the store through the use of mobile search technology; modeling of effective models of sales of goods on the basis of the laws and reasons of behavior of consumers revealed by means of artificial intelligence. The development of e-commerce is predicted in two scenarios – pessimistic and optimistic. To determine the projected volume of e-commerce in the pessimistic scenario, linear trends are built; and for the optimistic scenario the polynomial model of development of approximate dependence is used. It is expected that by the end of 2025 the volume of e-commerce will amount to 124 billion hryvnias (3.65 billion dollars) under the pessimistic scenario and 275 billion hryvnias (5.9 billion dollars) on the optimistic. Based on analysis of variance, it is determined that the relationship between the impact of Internet penetration on the volume of e-commerce is, but it is not very strong; that is, the development of e-commerce is influenced by other market factors.

Design and Development of a Mobile Robot for Search and Rescue Operations in Debris

After an earthquake, critical tasks during the search and rescue operations are to quickly detect the locations of victims and recover them. Additionally, effective communication between responders in the field and the emergency management center is also important to provide successful operations. All these tasks are mostly performed by humans and trained dogs in dangerous and risky situations. In this study, the design and development of a mobile search and rescue robot are presented. The design criteria are determined based on operational requirements and stakeholder expectations. Considering the specified design criteria, the mechanical and electrical designs of the mobile robot are performed and corresponding control, communication, and user interface software are developed. Designed parts of the robot are manufactured and the system integration is done. Then robot functionality tests are performed. The test results show that the performance of the developed search and rescue robot meets the desired performance criteria. The developed search and rescue robot has great potential to provide an efficient way to reduce losses of lives by decreasing operation time and for getting accurate information about the situation of the disaster area.

Analysis of attention on venture capital: A method of complex network on time series

Venture capital is an important force in promoting technological innovation and social progress. Research regarding the attention on venture capital can help understand the development and social influence of venture capital, which is conducive to the policy guidance and incentive to the industry. With the rapid development of information technology, the Internet has become the main source and dissemination channel of information, and search engines have become an important interface for information. Through a deep analysis of the search index of venture capital, we can find information that is more important. Based on the principle of time series visualization, we have transformed the time series data of attention on venture capital into complex networks and analyzed its network characteristics. We collected the Baidu index of Chinese venture capital from January 1, 2018 to November 25, 2019 and constructed a time series network based on PC plus mobile search, PC search, and mobile search. The results show that the convenience of the mobile terminal offers makes it the primary mode of tracking the industrial dynamics of venture capital, especially hot news. Relatively, PC terminal search is more stable than mobile search, more focused on industry reports, and refers to long-term followers of venture capital. Both degree distribution and centrality distribution of the three networks show that abnormal peaks and lows are few and the number of key time points in the time series of search on venture capital is insignificant. However, the clustering results show obvious segmentation effect that the peak effect of hot news is evident.

Will you go where you search? A deep learning framework for estimating user search-and-go behavior

Every day, people are using search engines for different purposes such as research, shopping, or entertainment. Among the behaviors of search engine users, we are particularly interested in search-and-go behavior, which intuitively corresponds to a simple but challenging question, i.e., will users go where they search? Accurately estimating such behavior can be of great importance for Internet companies to recommend point-of-interest (POI), advertisement, and route, as well as for governments and public service operators like metro companies to conduct traffic monitoring, crowd management, and transportation scheduling. Therefore, in this study, we first collect search log data and GPS log data with linked and consistent user ID from Yahoo! Japan portal application installed in millions of smart-phones and tablets. Then we propose a framework including a complete data-processing procedure and an end-to-end deep learning model to predict whether a user will check-in the searched place or not. Specifically, as users’ daily activities are considered to have high correlation with their travel, eating, and recreation decision in the future (i.e., go or not), Deep Spatial–Temporal Interaction Network (DeepSTIN) is elaborately designed to automatically learn the sophisticated spatiotemporal interactions between mobility data and search query data. Experimental results based on the standard metrics demonstrate that our proposed framework can achieve satisfactory performances on multiple real-world search scenarios.

GOING IN A DIFFERENT DIRECTION: CRITICAL ARTS-BASED APPROACHES TO GOOGLE MAPS

These days, wayfinding is often associated with ‘asking’ Google Maps for directions. Over one billion people per month use Google Maps and Google estimates that one-in-three mobile searches is location-related. Whose routes does Google Maps direct one towards and what types of pathways become naturalized? I approached these questions through performance- and drawing-based research. In the early 1960s, artist Stanley Brouwn stood on a street corner in Amsterdam and asked people for directions with pen and paper in hand. Over the course of the project, Brouwn amassed a collection of hand drawn maps. Now, in contemporary conditions of geomedia, I reactivated Brouwn’s work as an exploratory research project in Toronto, New York City, London, and Amsterdam. I also collected drawings and notes, spontaneously produced in situ, which I analyzed in relation to the location-awareness and real-time feedback of digital mapping technologies. The encounters produced notes and drawings that point to the information literacies used in street-level wayfinding and situate these in relation to the mobilities and spatial perceptions prioritised by Google Maps’ interface and affordances. The paper presents a theoretical framework to assess the types of calibrations at play when proprietary mapping platforms broker spatial information. The paper uses alternative research methods to explore how Google Maps is more than a guide but an orientation towards a Google spatial imaginary while at the same time complicated by the ad hoc layering of other wayfinding strategies.

Mobile APP Micro‐Usage during the mobile search

AbstractMobile users' micro‐usage behavior on smartphones has got researchers' attention, while current studies have not focused on the mobile search context. This study aimed to understand the characteristics of micro‐usage behavior in mobile search sessions and the triggers for them, through mixed methods. The findings revealed that micro‐usage mainly occurred on social and communication APPs. Searching for information about games and health can trigger more micro‐usage. Besides, chatting with others was the most important causes of micro‐usage. This study helps to better understand mobile usage behavior and micro‐usage in a search session.

Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study.

BackgroundSouth Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis.ObjectiveWe attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data.MethodsGoogle Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19–related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5.ResultsThe numbers of COVID-19–related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ≤29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ≥50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test–related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19–related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case–based model and potentially be used to predict epidemic curves.ConclusionsThe use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.

An Overview of Digital Marketing Practices in India

The most popular businesses adopt the Internet, not simply as another advertisement tool or marketing instrument. It is seen as a mechanism for transforming the companies and changing everything about the way computers and mobile devices function to serve their customer better and to connect with their suppliers. In short, digital marketing has sparked a business world revolution by pushing out some old established players and rising new leaders. The winners discover new market possibilities, enhanced ways to develop work and better ways to manage and run their companies. Despite this, the digital marketing entrepreneurs still have to take care of their clients and earn a profit to remain in the business. Web-based business success requires the entrepreneur to strike a balance between creating a digital marketing strategy that utilizes the strength of the Internet and meeting customer expectations of convenience and service. Consumer's present use of mobile devices, social media and search engines have led to developing integrated social –local-mobile strategies (SOLOMO). Entrepreneurs may use the SOLOMO strategy effectively as customers enhance their online activities by extensively using these three methods.

THE ROLE OF INTERNET ADVERTISEMENTS IN THE COMPANY ADVERTISING MANAGEMENT

Introduction. The national problem of timely delivery of goods from production to the population, the formation of demand for them, taking into account the socio-demographic characteristics of certain groups of consumers, the education of the rational needs of people requires advertising. Its role is especially growing in a market economy, competitive environment, constant updating of the range of goods, the complexity of the design of many household goods. Comprehensive information for the population about consumer properties and ways of using goods are the most important tasks of advertising. The purpose of this article is to research features of Internet advertising in the company advertising management. Results. The features of Internet advertising to promote products on the market is considered. The main types of Internet advertising and their features in the company management system are studied. The main advantages of Internet advertising and its shortcomings in the system of marketing tools to promote products on the market are highlighted. The volume of the Internet advertising market, the volume of the search advertising market in Ukraine, the share of social media and messengers, %, the share of Mobile in media Internet advertising, % were studied. It is established that the volume of the Internet advertising market in 2019 in Ukraine amounted to 12.6 billion hryvnias, which exceeds the results of 2018 by 35%, the share of mobile paid search in 2019 increased to 73% compared to 66% in 2018 and 56% in 2017. The share of advertising expenditures in social media and messengers in agencies continues to grow actively and in 2019 the scale was 45.5%, which is 11% more than in 2018. The share of mobile in online Internet advertising continues to grow and reached 47.7% in 2019. The share of digital video has slightly increased compared to 2018. Conclusions. As a means of communication between the company and its consumers, Internet advertising creates new opportunities, in particular related to the personification of contact and dialogue between both stakeholders. As a form of interpersonal communication, online advertising acts both as a means of self-expression and as a means of forming interest groups. Its role is also important in other spheres of society. Keywords: marketing, advertising management, online advertising, targeted advertising, social networks, key performance indicators, cost per click, cost per 1000 impressions.

Lessons Learned in Selection and Review of Depression Apps for Primary Care Settings

Depression is the leading cause of disability worldwide and is one of the most common mental health issues being addressed within primary care settings. Mobile apps, which can be used to help people manage their depressive symptoms, are rapidly developing. However, many challenges exist for clinicians and providers to simply select an appropriate app for use within target populations. The objectives of this article are as follows: (1) to describe the search processes that were used to identify depression-related mobile apps and (2) to describe the review process that was implemented to inform and evaluate the identified depression-related mobile health apps for use with our target population. A research team consisting of information technology researchers, primary and psychiatric care providers, and health care researchers completed two mobile app searches to identify depression-related apps which could be used for further exploration within an underserved integrated primary care setting. Sixteen mobile apps were narrowed down to 4 mobile apps, through a series of steps involving screening, collaboration of the interprofessional team, information technology expertise input, and mobile app evaluation tools. This article described the steps a research team used to search, screen, and assess mental health mobile apps for integrated primary care patients with depression. This step-by-step guide focused on depression-related apps; however, similar steps and principles identified in this guide can be applied to other health apps.

Fighting pandemics with digital epidemiology.

Fighting pandemics with digital epidemiology.

A neural integrator model for planning and value-based decision making of a robotics assistant

Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments.

Credibility assessment of good abandonment results in mobile search

Search engine optimization allows for users’ needs to be directly met by result snippets or a “knowledge map” without clicking any results. This behavior is called “good abandonment” and is found to frequently occur during mobile searching. Users exhibit such a behavior when they trust the result that addresses their information need without bothering to click it. Therefore, this study examines how users judge a result's credibility without clicking. This study proposes a model for assessing the credibility of good abandonment results, making a hypothesis about the measures that may affect credibility assessments in mobile searches. A credibility assessment experiment was conducted to collect users’ eye movements, perceived credibility and feedback on different credibility measures. Users’ search behaviors were recorded by a screen recorder, in order to see whether a search was good abandonment. Then the initially proposed model was validated in terms of users’ perceived credibility, search behaviors and feedback, and further improved. The revised model found that the credibility assessment of good abandonment results in mobile searching is determined by six credibility measures distributed across three aspects of content, operator and design. Content-related measures show that users tend to believe the results if there is detailed and updated context information and the content is neutral. Operator-related measures indicate the impact of trust in the search engine on the credibility assessment. Design-related measures indicate that users tend to trust results with interactive functions and optimal layouts. How each of the six measures influence users’ assessment of credibility is discussed in this paper.

Autonomous Search of Radioactive Sources through Mobile Robots.

The research of robotic autonomous radioactivity detection or radioactive source search plays an important role in the monitoring and disposal of nuclear safety and biological safety. In this paper, a method for autonomously searching for radioactive sources through mobile robots was proposed. In the method, by using a partially observable Markov decision process (POMDP), the search of autonomous unknown radioactive sources was realized according to a series of radiation information measured by mobile robot. First, the factors affecting the accuracy of radiation measurement during the robot’s movement were analyzed. Based on these factors, the behavior set of POMDP was designed. Secondly, the parameters of the radioactive source were estimated in the Bayesian framework. In addition, through the reward strategy, autonomous navigation of the robot to the position of the radiation source was achieved. The search algorithm was simulated and tested, and the TurtleBot robot platform was used to conduct a real search experiment on the radio source Cs-137 with an activity of 37 MBq indoors. The experimental results showed the effectiveness of the method. Additionally, from the experiments, it could been seen that the robot was affected by the linear velocity, angular velocity, positioning accuracy and the number of measurements in the process of autonomous search for the radioactive source. The proposed mobile robot autonomous search method can be applied to the search for lost radioactive sources, as well as for the leakage of substances (nuclear or chemical) in nuclear power plants and chemical plants.

Device-to-device streaming video caching for cellular download cost reduction

Abstract This paper proposes a Device-to-Device cooperative caching framework for streaming video consumption by mobile users. For downloading video with cooperative caching, a mobile device first searches within its neighboring Bluetooth-connected peers for whole or parts of the requested video before downloading them from a Content Provider’s server. A specific cooperative caching framework, namely, value-based caching is proposed in which the value of caching a hierarchically coded streaming video segment is defined for given pricing and video sharing models. Within this framework, we develop an Adaptive Quality (AQ) provisioning algorithm that minimizes the overall video content provisioning cost incurred due to the bandwidth usage of a cellular network. Detailed simulation experiments for functional validation and performance evaluation in comparison with other comparable caching strategies are then performed under different protocols and network parameters. It is shown that the proposed mechanism is able to appreciably reduce the overall video provisioning cost in the presence of end-user mobility.

"Revisiting information retrieval tasks with user behavior models" by Yiqun Liu and Jiaxin Mao with Martin Vesely as coordinator

Analyzing and modeling user behavior in the Information Retrieval (IR) process is important for IR research. In this paper, we propose a new research schema in IR research. In this schema, we first investigate humans' cognitive process when completing a specific IR task and build cognitive models for that task. The findings in the investigation and the proposed cognitive models are then utilized to improve machines' performance in the IR task. Through this research schema, we revisit three IR tasks. In the first study, we carefully analyze users' clicking behavior in mobile search and propose a mobile click model to extract relevance feedback from the mobile search logs. In the second study, we conduct an eye-tracking study to investigate how human assessors read a document during relevance judgment task and adopt the findings in building a novel retrieval model that can better approximate humans' relevance judgment. In the last study, we conduct another eye-tracking study to investigate humans' reading behavior when completing the reading comprehension task. We build a prediction model for user attention and leverage the predicted attention signals to improve the machine reading comprehension model. By successfully adopting this research schema to three different IR tasks, we demonstrate its effectiveness and generalizability.

Computer Anxiety as Correlate of Use of Information Technology Tools among Undergraduates of Library and Information Science in Tai Solarin University of Education, Ijagun, Ogun State

The study was designed to investigate computer anxiety as a correlate of use of Information Technology tool by undergraduates of Library and Information Science in Tai Solarin University of Education (TASUED), Ijagun, Ogun State. Descriptive survey design was adopted while Stratified Random Sampling Technique was used to select 169 undergraduates of LIS. A questionnaire designed by the researchers was used for collecting data. Out of the 169 copies of the questionnaire administered 120 copies were returned and used for the analysis. Data was analyzed by Simple percentages, frequency, Mean and Standard Deviation and Pearson product moment correlation. Findings show that the level of computer anxiety among LIS undergraduates is high. The available IT tools among LIS undergraduates are mobile technology, laptops, CD-ROM, printers and internet search engines. The most frequency utilized IT tools among LIS undergraduate are mobile technology and laptops. The impact of computer anxiety on students’ utilization of IT tools is high. The major constraints of computer anxiety towards the utilization of IT tools among LIS undergraduates are level of computer anxiety and lack of conducive environment. A null hypothesis was tested which revealed that there is a significant relationship between computer anxiety and students’ utilization of IT tools among the LIS undergraduates surveyed. The study therefore recommends that the management of TASUED should provide more conducive learning environment, adequate IT infrastructural facilities, computer literacy should be encouraged to help lower anxiety and reduce stress in an IT environment.Keywords: computer anxiety, computer literacy, Tai Solarin University, undergraduates

Answering recreational web searches with relevant things to do results

Recreational queries from users searching for places to go and things to do or see are very common in web and mobile search. Users specify constraints for what they are looking for, like suitability for kids, romantic ambiance or budget. Queries like “restaurants in New York City” are currently served by static local results or the thumbnail carousel. More complex queries like “things to do in San Francisco with kids” or “romantic places to eat in Seattle” require the user to click on every element of the search engine result page to read articles from Yelp, TripAdvisor, or WikiTravel to satisfy their needs. Location data, which is an essential part of web search, is even more prevalent with location-based social networks and offers new opportunities for many ways of satisfying information seeking scenarios.In this paper, we address the problem of recreational queries in information retrieval and propose a solution that combines search query logs with LBSNs data to match user needs and possible options. At the core of our solution is a framework that combines social, geographical, and temporal information for a relevance model centered around the use of semantic annotations on Points of Interest with the goal of addressing these recreational queries. A central part of the framework is a taxonomy derived from behavioral data that drives the modeling and user experience. We also describe in detail the complexity of assessing and evaluating Point of Interest data, a topic that is usually not covered in related work, and propose task design alternatives that work well.We demonstrate the feasibility and scalability of our methods using a data set of 1B check-ins and a large sample of queries from the real-world. Finally, we describe the integration of our techniques in a commercial search engine.