Mobile Search Research Articles

Book review: Dan Wu and Shaobo Liang, Mobile Search Behavior: An In-depth Analysis Based on Contexts, APPs, and Devices

This review looks at a book that aims to provide further understanding of search behaviour on mobile devices, as people act through not only web browsers, but also specialist mobile apps, transition between those apps and also transition across devices. According to the authors the book addresses a number of perceived gaps in the mobile search behaviour literature. These include understanding mobile search behaviours from the perspective of user sessions, with tendencies for the current literature to focus on analysing patterns as a whole in large aggregated search logs of single search engines, rather than by individual sessions and multiple apps. The book also premises the increased importance of specialist search apps over and above traditional web browsers.

Shaping of Shared Autonomous Solutions With Minimal Interaction.

A fundamental problem in creating successful shared autonomy systems is enabling efficient specification of the problem for which an autonomous system can generate a solution. We present a general paradigm, Interactive Shared Solution Shaping (IS3), broadly applied to shared autonomous systems where a human can use their domain knowledge to interactively provide feedback during the autonomous planning process. We hypothesize that this interaction process can be optimized so that with minimal interaction, near-optimal solutions can be achieved. We examine this hypothesis in the space of resource-constrained mobile search and surveillance and show that without directly instructing a robot or complete communication of a believed target distribution, the human teammate is able to successfully shape the generation of an autonomous search route. This ability is demonstrated in three experiments that show (1) the IS3 approach can improve performance in that routes generated from interactions in general reduce the variance of the target detection performance, and increase overall target detection; (2) the entire IS3 autonomous route generation system's performance, including cost of interaction along with movement cost, experiences a tradeoff between performance vs. numbers of interactions that can be optimized; (3) the IS3 autonomous route generation system is able to perform within constraints by generating tours that stay under budget when executed by a real robot in a realistic field environment.

Mobile Search Development Research

In modern times, the development of the Internet industry is very rapid, especially mobile phone usage increase and the steady development of 4G communication network, and the more advanced technology development and application of mobile search become an indispensable part of People’s Daily life, its characteristics of convenience, timeliness, to better meet the needs of the relevant information access.

Evaluation in Contextual Information Retrieval

Context such as the user’s search history, demographics, devices, and surroundings, has become prevalent in various domains of information seeking and retrieval such as mobile search, task-based search, and social search. While evaluation is central and has a long history in information retrieval, it faces the big challenge of designing an appropriate methodology that embeds the context into evaluation settings. In this article, we present a unified summary of a wide range of main and recent progress in contextual information retrieval evaluation that leverages diverse context dimensions and uses different principles, methodologies, and levels of measurements. More specifically, this survey article aims to fill two main gaps in the literature: First, it provides a critical summary and comparison of existing contextual information retrieval evaluation methodologies and metrics according to a simple stratification model; second, it points out the impact of context dynamicity and data privacy on the evaluation design. Finally, we recommend promising research directions for future investigations.

Radiation anomaly detection and classification with Bayes Model Selection

We present a new method for radiation anomaly detection that is based on Bayes Model Selection (BMS), together with models for gamma-radiation measurements from benign and threat sources. The method estimates the relative odds of pairs of such models, with the aim of supporting related hypotheses about the nature of the underlying source material. We also discuss partial optimization of the parameters in the models. The method allows measurements to be broadly categorized and screened for sources of interest in real time, a property that should improve the efficiency of mobile search or unattended monitoring operations

The Influence of Label Design on Search Performance and Noticeability in Wide Field of View Augmented Reality Displays.

In Augmented Reality (AR), search performance for outdoor tasks is an important metric for evaluating the success of a large number of AR applications. Users must be able to find content quickly, labels and indicators must not be invasive but still clearly noticeable, and the user interface should maximize search performance in a variety of conditions. To address these issues, we have set up a series of experiments to test the influence of virtual characteristics such as color, size, and leader lines on the performance of search tasks and noticeability in both real and simulated environments. We evaluate two primary areas, including 1) the effects of peripheral field of view (FOV) limitations and labeling techniques on target acquisition during outdoor mobile search, and 2) the influence of local characteristics such as color, size, and motion on text labels over dynamic backgrounds. The first experiment showed that limited FOV will severely limit search performance, but that appropriate placement of labels and leaders within the periphery can alleviate this problem without interfering with walking or decreasing user comfort. In the second experiment, we found that different types of motion are more noticeable in optical versus video see-through displays, but that blue coloration is most noticeable in both. Results can aid in designing more effective view management techniques, especially for wider field of view displays.

The Estimation of Compensating Wage Differentials: Lessons From the Deadliest Catch

ABSTRACTI use longitudinal survey data from commercial fishing deckhands in the Alaskan Bering Sea to provide new insights on empirical methods commonly used to estimate compensating wage differentials and the value of statistical life (VSL). The unique setting exploits intertemporal variation in fatality rates and wages within worker-vessel pairs caused by a combination of weather patterns and policy changes, allowing identification of parameters and biases that it has only been possible to speculate about in more general settings. I show that estimation strategies common in the literature produce biased estimates in this setting, and decompose the bias components due to latent worker, establishment, and job-match heterogeneity. The estimates also remove the confounding effects of endogenous job mobility and dynamic labor market search, narrowing a conceptual gap between search-based hedonic wage theory and its empirical applications. I find that workers’ marginal aversion to fatal risk falls as risk levels rise, which suggests complementarities in the benefits of public safety policies. Supplementary materials for this article are available online.

Factors Influencing Mobile Search Engine Users' Continuance Behavior Under the Context of Service Harm Crisis Event

Post-adoption behavior has become one of the research hotspots in recent years. The purpose of this article is to research mobile search engine user's usage behaviour under the context of service harm crisis. Hypotheses and theoretical model are proposed by integrating expectation confirmation model of IS continuance, technology acceptance model, perceived risk theory, attribution theory and habit theory. The results indicated that continuance intention along with satisfaction and habit are important determinants of continuance behaviour. Perceived usefulness, perceived risk and continuance attitude have significant effect on continuance intention while attitude has the strongest effect. Confirmation promotes perceived usefulness, satisfaction and perceived ease of use which has an indirectly significant influence on satisfaction via perceived usefulness. Attribution of responsibilities has been found to have positive effect on perceived risk. Implications and contributions are provided.

Meine Geologie-Ziele mobil – mit dem UmweltAtlas Bayern im Taschenformat

Meine Geologie-Ziele mobil – mit dem UmweltAtlas Bayern im Taschenformat

DeepProduct

Features extracted by deep networks have been popular in many visual search tasks. This article studies deep network structures and training schemes for mobile visual search. The goal is to learn an effective yet portable feature representation that is suitable for bridging the domain gap between mobile user photos and (mostly) professionally taken product images while keeping the computational cost acceptable for mobile-based applications. The technical contributions are twofold. First, we propose an alternative of the contrastive loss popularly used for training deep Siamese networks, namely robust contrastive loss, where we relax the penalty on some positive and negative pairs to alleviate overfitting. Second, a simple multitask fine-tuning scheme is leveraged to train the network, which not only utilizes knowledge from the provided training photo pairs but also harnesses additional information from the large ImageNet dataset to regularize the fine-tuning process. Extensive experiments on challenging real-world datasets demonstrate that both the robust contrastive loss and the multitask fine-tuning scheme are effective, leading to very promising results with a time cost suitable for mobile product search scenarios.

Mobile Search Behavious: An In-depth Analysis based on Contexts, APPs, and Devices

Mobile Search Behavious: An In-depth Analysis based on Contexts, APPs, and Devices

The Characteristics of Voice Search

The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries rather than type them. While this kind of voice search is still in its infancy, it is gradually becoming more widespread. In this article, we report a comprehensive voice search query log analysis of a commercial web search engine’s mobile application. We compare voice and text search by various aspects, with special focus on the semantic and syntactic characteristics of the queries. Our analysis suggests that voice queries focus more on audio-visual content and question answering and less on social networking and adult domains. In addition, voice queries are more commonly submitted on the go. We also conduct an empirical evaluation showing that the language of voice queries is closer to natural language than the language of text queries. Our analysis points out further differences between voice and text search. We discuss the implications of these differences for the design of future voice-enabled web search tools.

인공지능 기반 모바일 여행 정보 특성에 따른 소비자 만족 및 행동의도에 대한 연구

인공지능 기반 모바일 여행 정보 특성에 따른 소비자 만족 및 행동의도에 대한 연구

Meta-Lamarckian learning in multi-objective optimization for mobile social network search

Mobile Social Networks (MSNs) have recently brought a revolution in socially-oriented applications and services for mobile phones. In this paper, we consider the search problem in a MSN that aims at simultaneously maximizing the user's search outcome (recall) and mobile phone performance (battery usage). Because of the conflicting nature of these two objectives, the problem is dealt within the context of Multi-Objective Optimization (MOO). Our proposed approach hybridizes a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) with a Meta-Lamarckian (ML) learning strategy that learns from the problem's properties and objective functions. The ML strategy is devised for adaptively select the best performing local search heuristic for each case, from a pool of general-purpose heuristics, so as to locally optimize the solutions during the evolution. We evaluated our propositions on a realistic multi-objective MSN search problem using trace-driven experiments with real mobility and social patterns. Extensive experimental studies reveal that the proposed method successfully learns the behaviour of individual local search heuristics during the evolution, adaptively follows the pattern of the best performing heuristics at different areas of the objective space and offers better performance in terms of both convergence and diversity than its competitors.The proposed Meta-Lamarckian based MOEA does not utilize any problem-specific heuristics, as most cases in the literature do, facilitating its applicability to other combinatorial MOO problems. To test its generalizability the proposed method is also evaluated on various test instances of the well-studied multi-objective Permutation Flow Shop Scheduling Problem.

Augmented keyword search on spatial entity databases

In this paper, we propose a new type of query that augments the spatial keyword search with an additional boolean expression constraint. The query is issued against a corpus of structured or semi-structured spatial entities and is very useful in applications like mobile search and targeted location-aware advertising. We devise three types of indexing and filtering strategies. First, we utilize the hybrid $$\hbox {IR}^2$$ -tree and propose a novel hashing scheme for efficient pruning. Second, we propose an inverted index-based solution, named BE-Inv, that is more cache concious and exhibits great pruning power for boolean expression matching. Our third method, named SKB-Inv, adopts a novel two-level partitioning scheme to organize the spatial entities into inverted lists and effectively facilitate the pruning in the spatial, textual, and boolean expression dimensions. In addition, we propose an adaptive query processing strategy that takes into account the selectivity of query keywords and predicates for early termination. We conduct our experiments using two real datasets with 3.5 million Foursquare venues and 50 million Twitter geo-profiles. The results show that the methods based on inverted index are superior to the hybrid $$\hbox {IR}^2$$ -tree; and SKB-Inv achieves the best performance.

PRMS: A Personalized Mobile Search Over Encrypted Outsourced Data

At present, searchable encryption is a very promising direction in the field of cloud computing. However, most existing works focus on keyword-based search schemes and regardless of personalized search needs, a keyword-based search does not take into account the user’s location information and cannot exactly match users’ search intentions. Since location information is very important in mobile searches, we propose a personalized mobile search (PRMS) over encrypted outsourced data, and we convert the user’s location information into distance information to generate the user location model, forming a location query matrix with user location information. The matrix is then used to encrypt the user’s location query and conceal the location information in the location query matrix. In this paper, we combine a user’s interest preference and location information in personalized searches over encrypted outsourced data, adopt the law of universal gravitation to calculate scores of files in the cloud, and return the first $K$ results with the highest gravitational forces. For the first time, we propose a PRMS-improvement scheme that is applied to an encryption method to build the content index and location index, which can greatly reduce the time for model construction. Through the PRMS scheme, we realized personalized mobile searches based on user’s interests and location information.

Urban Roads and Non-Motorized Transport: The Barrier Effect and Challenges in the Search for Sustainable Urban Mobility

Abstract The aim of this study is to propose actions that can minimize the harmful effects that large-scale urban roads can cause on dislocations through non-motorized transport modes, especially damages related to the Barrier Effect, with the goal on improving the quality of urban environment. For the purposes of theoretical basis, the study makes a literary referencial research on the concepts and solutions in sustainable urban mobility, non motorized transport and the concepts and consequences related to the Barrier Effect, in order to identify the main variables and indicators referring to the themes. From the study of these revisions, it was possible to elaborate a research method, based on the application of opinion polls on the area affected by the Barrier Effect, divided into two stages: i) identification of the factors that generate the Barrier Effect, recommended and proposed by Likert and the use of the Successive Intervals Method; ii) application of the stated preference technique, whose contribution aims to propose solutions in sustainable mobility for non-motorized transport in order to minimize the consequences of the Barrier Effect. The results allow to infer that the Barrier Effect is illustrated in the study area, the difficulty of crossing the lane and the sensation of insecurity are the major influencers of this segregation. There are some causes of this infect but the most significant are the lack of crossing options and the inadequacy of sidewalks and bicycle paths.

Advertising and Labor Market Matching: A Tour through the Times

Surveying employment-related newspaper advertisements over several centuries, we identify four eras (neither workers nor firms posted ads, mostly workers posted ads, mostly firms posted ads, and both parties regularly posted ads). These eras can be understood in the context of the equilibrium of a matching model that incorporates strategic interactions by both sides of the labor market. Potential explanations for transitions across eras include increasing literacy rates, expansion of social insurance programs, growth in the labor force and firm size, reduction in mobility costs and search frictions, and the internet.

DeepSearch

Content-based image retrieval (CBIR) is one of the most important applications of computer vision. In recent years, there have been many important advances in the development of CBIR systems, especially Convolutional Neural Networks (CNNs) and other deep-learning techniques. On the other hand, current CNN-based CBIR systems suffer from high computational complexity of CNNs. This problem becomes more severe as mobile applications become more and more popular. The current practice is to deploy the entire CBIR systems on the server side while the client side only serves as an image provider. This architecture can increase the computational burden on the server side, which needs to process thousands of requests per second. Moreover, sending images have the potential of personal information leakage. As the need of mobile search expands, concerns about privacy are growing. In this article, we propose a fast image search framework, named DeepSearch, which makes complex image search based on CNNs feasible on mobile phones. To implement the huge computation of CNN models, we present a tensor Block Term Decomposition (BTD) approach as well as a nonlinear response reconstruction method to accelerate the CNNs involving in object detection and feature extraction. The extensive experiments on the ImageNet dataset and Alibaba Large-scale Image Search Challenge dataset show that the proposed accelerating approach BTD can significantly speed up the CNN models and further makes CNN-based image search practical on common smart phones.

Mobile Visual Search Based on Histogram Matching and Zone Weight Learning

In this paper, we propose a novel image retrieval algorithm for mobile visual search. At first, a short visual codebook is generated based on the descriptor database to represent the statistical information of the dataset. Then, an accurate local descriptor similarity score is computed by merging the tf-idf weighted histogram matching and the weighting strategy in compact descriptors for visual search (CDVS). At last, both the global descriptor matching score and the local descriptor similarity score are summed up to rerank the retrieval results according to the learned zone weights. The results show that the proposed approach outperforms the state-of-the-art image retrieval method in CDVS.