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This paper aims to propose an automated mobile app labeling framework based on a novel app classification scheme that is aligned with users’ primary motivations for using smartphones. The study addresses the gaps in incorporating the needs of users and other context information in app classification as well as recommendation systems.

Based on a corpus of mobile app descriptions collected from Google Play store, this study applies extensive text analytics and topic modeling procedures to profile mobile apps within the categories of the classification scheme. Sufficient number of representative and labeled app descriptions are then used to train a classifier using machine learning algorithms, such as rule-based, decision tree and artificial neural network.

Experimental results of the classifiers show high accuracy in automatically labeling new apps based on their descriptions. The accuracy of the classification results suggests a feasible direction in facilitating app searching and retrieval in different Web-based usage environments.

As a common challenge in textual data projects, the problem of data size and data quality issues exists throughout the multiple phases of experiments. Future research will extend the data collection scope in many aspects to address the issues that constrained the current experiments.

These empirical experiments demonstrate the feasibility of textual data analysis in profiling apps and user context information. This study also benefits app developers by improving app descriptions through a better understanding of user needs and context information. Finally, the classification framework can also guide practitioners in customizing products and services beyond mobile apps where context information and user needs play an important role.

Given the widespread usage and applications of smartphones today, the proposed app classification framework will have broader implications to different Web-based application environments.

While there have been other classification approaches in the literature, to the best of the authors’ knowledge, this framework is the first study on building an automated app labeling framework based on primary motivations of smartphone usage.

The purpose of this paper is to determine the usability of the mobile library App of Chongqing University and to provide recommendations for improving the experience of App users.

Usability testing, which comprised pre-test questionnaires, accomplishing tasks, and post-test surveys, was conducted in this study. The effectiveness and efficiency of the App, as well as user satisfaction with it, were measured.

The mobile App was proven effective but the efficiency of the App required improvement. With regard to user satisfaction, the factors “clarity” and “usefulness” received the lowest and highest scores, respectively. The descriptions of mobile services were unclear and confused users; nonetheless, the services provided by this App were appealing and useful to the users. Based on the measured user experience, this study proposed several recommendations for enhancing the usability of the App.

An increasing number of domestic and foreign libraries have begun to use mobile Apps to provide new services to patrons. In the future, smartphones are likely to become crucial to the delivery of information services. Given the extensive use of the Super Star Mobile Library system adopted by Chongqing University Library in China, the usability of such a system must be investigated. Improving the usability of the mobile library App can help enhance user experience.

Mobile users increasingly employ location-based map searches in their daily lives. However, it is still relatively unknown about mobile users’ map related search behaviors. The purpose of this paper is to discover the interactions between the users and mobile map search systems, to reveal the shortcomings of existing mobile map search functions, and to propose improvement suggestions.

Based on a set of controlled user experiments performed on the Baidu mobile phone map, this paper empirically examines users’ location-based mobile search behaviors, such as timing, metering, judging and so on. This paper also conducts statistical correlation tests to generate relation tables and diagrams regarding each variable, for example, the relation between the retrieval time and the retrieval steps.

The results indicate that mobile map users have two important characteristics in their search behaviors: first, mobile map users always follow the single search path. Second, the mobile map search efficiency of users is always low.

The situation simulation testing method is mainly used for the construction of a mobile information search behavior environment, which may make the users be nervous and have some effect on the search efficiency.

Based on the identification of user behaviors, this paper provides suggestions to optimize and improve mobile map search systems.

This paper studies users’ mobile map search behavior based on location and explores the features of user behavior from the perspective of human-computer interaction.

Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

This paper aims to describe the Tunepal project as an example of a music information retrieval (MIR) system that is having an impact on how musicians access, learn and play traditional Irish music around the world.

This paper describes the functionality of the Tunepal system: consisting of the tune corpus, the web site tunepal.org and mobile apps supporting iOS and Android OS. Tunepal facilitates query‐by‐title and query‐by‐playing music (QBP) searches and allows a musician to retrieve and playback scores amongst other supported functions.

Tunepal has been favorably received and musicians report that the system is being used in a variety of scenarios including archiving and the preparation of sleeve notes for commercial recordings. Tunepal has a growing user base in 25 countries.

The comprehensive tune corpus (over 16,000 compositions), the query‐by‐playing technology and the fact that the mobile apps provide access to the corpus in situ in traditional music sessions and classes make this project uniquely useful.

This paper aims to find out how people use hashtags as a medium of information retrieval and dissemination, and how they are used in social media tools, such as Instagram.

A quantitative question estimated the participants’ use of the hashtags during the workshop. Statistical data of the participants and their posts were collected from social network analysis tools. The posts that included the workshop’s designated hashtags were retrieved, recorded, coded and analysed to collect qualitative data.

In total, 74 (46 per cent) participants used the workshop’s hashtags to share posts, the retrieval of the hashtags declined by time and Google search engine retrieved the maximum results. It was found that a hashtag would be common when associated with descriptors, and that its use depends on its popularity, followers and its survival time. Finally, hashtags connect people, allow them to express their enthusiasm to reveal common interests and networks them through social media tools such as Instagram.

The research limitations were in relation to the participants’ demographic information, the non-identification of their gender and hashtags being misspelt.

The research project summarises the experiences that social media has made connecting easier through the right use of hashtags by providing 24/7 free feedback, the possibility to exchange ideas and by their involvement in promoting and organising events. It also indicates interaction among people sharing the same interest by retrieving subject-based hashtags.

When retrieving information related to hashtags, it is recommended that multi-retrieval systems, social media tools and search engines should be consulted and not depend on a solo system or tool. Future research is recommended in search for a multi-retrieval social media and search engine tool that standardises the use of hashtags and will retrieve information from different platforms.

Cross-app interactive search has become the new normal, but the characteristics of their tactic transitions are still unclear. This study investigated the transitions of daily search tactics during the cross-app interaction search process.

In total, 204 young participants' impressive cross-app search experiences in real daily situations were collected. The search tactics and tactic transition sequences in their search process were obtained by open coding. Statistical analysis and sequence analysis were used to analyze the frequently applied tactics, the frequency and probability of tactic transitions and the tactic transition sequences representing characteristics of tactic transitions occurring at the beginning, middle and ending phases. 

Creating the search statement (Creat), evaluating search results (EvalR), evaluating an individual item (EvalI) and keeping a record (Rec) were the most frequently applied tactics. The frequency and probability of transitions differed significantly between different tactic types. “Creat? EvalR? EvalI? Rec” is the typical path; Initiate the search in various ways and modifying the search statement were highlighted at the beginning phase; iteratively creating the search statement is highlighted in the middle phase; Moreover, utilization and feedback of information are highlighted at the ending phase. 

The present study shed new light on tactic transitions in the cross-app interactive environment to explore information search behaviour. The findings of this work provide targeted suggestions for optimizing APP query, browsing and monitoring systems.

This study explores the importance of application search engine (ASE) technology in the omni-channel strategy. For this purpose, this chapter firstly explains the concepts of the omni-channel and the search engines and the importance of them. Then, omni-channel in the framework of ASEs is discussed. Finally, recommendations for further researches are presented.

This paper aims to present the results of the second stage of a research project aiming to develop a bilingual interface for the retrieval of digital images. The main objective of this phase was to investigate the roles and usefulness of search characteristics and functionalities for image retrieval in a bilingual context.

A bilingual (English and French) questionnaire containing closed and open questions was developed and administered to two groups of participants: 20 English-speaking and 20 French-speaking respondents. The quantitative data was analysed according to statistical methods while the content of the open-ended questions was analysed and coded to identify emergent themes.

This study shows that the image search process still presents difficulties and frustration from the image searchers' point-of-view. The findings established that keyword search remains the main method compared with the use of predefined categories or searching with a similar image or a drawing. They emphasised the importance of several functionalities as an integral part of the image search process and revealed the importance of being able to search for images with words extracted from more than one language.

The main contribution of this exploratory study is to provide an understanding of how real users search for images. Combined with the exploration of best practices for image retrieval, the analysis of real image searchers' behaviours provides the foundation for the initial organisation of the search interface model we will develop in the ultimate stage of the research project.

This study aims to examine the effects of the proposed mobile Web library application (MWLA) on the search experience and its impact on learners’ engagement, interaction and overall learning outcomes within an institutional repository. Furthermore, the study investigates learners’ acceptance of the MWLA system.

The study suggests implementing an MWLA with Algolia’s search service to improve the institutional repository and enhance learners’ access to reliable information. It involved an experiment with 85 undergraduate students divided into experimental and control groups (CGs), where the experimental group (EG) used MWLA for search tasks, and the CG used the traditional library website. The study evaluated the acceptance and learning behaviours of the EG towards MWLA, considering factors such as usefulness, ease of use, mobility, accessibility, satisfaction and intention to use.

The findings of this study provide empirical evidence that the EG, which used the MWLA, demonstrated superior performance compared to the CG across all institutional repository collections, resulting in improved learning outcomes. Participants were highly satisfied with MWLA and found it user-friendly and beneficial for improving search skills. MWLA’s portability and accessibility motivated active learner engagement.

The powerful search bar of MWLA significantly enhanced learners’ search efficiency, resulting in more effective retrieval of relevant materials. Moreover, learners who actively engaged with previews and full-text content, using appropriate keywords and syntax, achieved higher scores and were more likely to access previews, abstracts and full texts of documents using the sorting-by-year or by-advisor feature.