Journal Information
Information Processing & Management (IPM)
http://www.journals.elsevier.com/information-processing-and-management/
Impact Factor:
3.444
Publisher:
ELSEVIER
ISSN:
0306-4573
Viewed:
9725
Tracked:
29

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Call For Papers
Information Processing & Management is devoted to refereed reporting of:

1. Basic and applied research in information science, computer science, cognitive science and related areas that deals with: the generation, representation, organization, storage, retrieval, and use of information; the nature, manifestations, behavior, and effects of information and knowledge; communication and distribution of information and knowledge; and human information behavior.

2. Experimental and advanced processes, related to: information retrieval (IR); digital libraries; knowledge organization and distribution; digitized contents - text, image, sound and multimedia processing; and human-computer interfaces in information systems. Implementations in information retrieval systems and a variety of information systems, networks, and contexts. Related evaluation.

3. Management of information resources, services, systems and networks, and digital libraries. Related studies of the economics of information and the principles of information management.

The aim is to provide an international forum for advanced works and critical analysis in these interdependent and interdisciplinary areas. Invited are original papers and critical reviews of trends reporting on:
• Progress in the theory, principles, and procedures in information processing, particularly involving information retrieval; search engines; knowledge and distributed intelligence; information representation, classification, extraction, filtering and summarization; question answering; information navigation, browsing and visualization; and human-computer interaction in information systems.
• Research on the formal characteristics and properties of information and knowledge and the associated processes of communication among humans and between humans and machines. Includes studies of human information needs, seeking, searching, and use; and bibliometric and infometric studies of the structural and statistical properties of information artifacts.
• Modeling and evaluation of information systems performance, particularly of information retrieval systems, knowledge systems, and digital libraries. Studies of their effectiveness, efficiency, value, or impact.
• Studies in management and economics of information and information systems. Use of information for decision making and problem solving.
• Studies in information policies. Data and issues relevant to information policies on organizational, national, and international levels. Derivation and use of information indicators.
Last updated by Dou Sun in 2018-10-19
Special Issues
Special Issue on Methods and applications in the analysis of social data in healthcare
Submission Date: 2019-10-30

The growing availability and accessibility of diverse and relevant health-related data resources, and the rapid proliferation of technological developments in data analytics is contributing to make the most of extracting the power of these datasets, to improve diagnosis and decision making, shorten the development of new drugs from discovery to marketing approval, facilitate early outbreak detection, improve healthcare professionals training and reduce costs to name but a few examples Extracting the knowledge to make this a reality is still a daunting task: on the one hand, data sources are not integrated, they contain private information and are not structured. On the other hand, we still lack context- and privacy-aware algorithms to extract the knowledge after a proper curation and enrichment of the datasets. In recent years technology has made it possible not only to get data from many healthcare settings (hospitals, primary care centers, laboratories, etc.), it also allows information to be obtained from the society itself (sensors, Internet of Things (IoT) devices, social networks, etc.). For instance, social media environments are a new source of data coming from all the community levels. For this reason, the organization of the current special issue responds to the necessity in collecting the last efforts that have been made in these areas of research. The special issue aims to publish high-quality research papers focused on the analytics of social data related to healthcare as well as those studies and works that include the processes needed to perform such analytics. Topics and scope of the proposed special issue The topics to be covered include, but are not limited to: · Challenges in social data analytics in healthcare: o i) data management o ii) data curation o iii) opinion mining and sentiment analysis o iv) privacy-aware data mining algorithms o v) data quality and veracity o vi) natural language processing and text mining o vii) semantics o viii) trends in discovery and analysis o ix) graph mining and community detection o x) social sensors o xi) IoT devices · Applications in social data analytics in healthcare: o i) epidemiological analysis o ii) outbreak detection o iii) human behavior o iv) medical skills and education o v) personalized medicine o vi) diagnosis, prognosis and prognostics
Last updated by Dou Sun in 2019-07-11
Special Issue on Methods and applications in the analysis of social data in healthcare
Submission Date: 2019-10-30

Topics and scope of the proposed special issue The topics to be covered include, but are not limited to: • Challenges in social data analytics in healthcare: o i) data management o ii) data curation o iii) opinion mining and sentiment analysis o iv) privacy-aware data mining algorithms o v) data quality and veracity o vi) natural language processing and text mining o vii) semantics o viii) trends in discovery and analysis o ix) graph mining and community detection o x) social sensors o xi) IoT devices • Applications in social data analytics in healthcare: o i) epidemiological analysis o ii) outbreak detection o iii) human behavior o iv) medical skills and education o v) personalized medicine o vi) diagnosis, prognosis and prognostics Target authors and contributors The special issue is mainly oriented to the authors that have accepted papers submitted to the IEEE International Symposium on Computer-Based Medical Systems (CBMS 2019). However, other authors which have not participated in the conference can also submit their papers. Review procedure The review will be done by at least two reviewers using a double-blind peer review process. In the case that disagreement between both reviewers take place, a third reviewer or one of the editors will review the paper to make a decision. Important dates Since the CBMS conference will take place on June 2019 our tentative schedule will be the following: • Manuscript submission: October 30th, 2019. • Author notification: December 15th, 2019. • Special issue publication: About January 2020. Guest editors Alejandro Rodríguez-González, Universidad Politécnica de Madrid, Spain. Sebastian Ventura Soto, Universidad de Córdoba, Spain. Paolo Soda, Università Campus Bio-Medico di Roma, Italy. Jesualdo Tomás Fernández-Breis, Universidad de Murcia, IMIB- Arrixaca, Spain.
Last updated by Dou Sun in 2019-01-05
Special Issue on AI driven Information Discovery in eCommerce
Submission Date: 2019-11-15

Search, ranking, and recommendation have applications ranging from traditional web search, to document databases, to vertical search systems. In the age of big data, eCommerce websites have accumulated large amounts of user personal information and behavioral data. Moreover, human-generated and machine-generated business data has been experiencing an exponential growth. This calls for sophisticated technologies from a wide spectrum of areas including information retrieval, machine learning, artificial intelligence, statistics, econometrics, and psychology, to explore how to effectively take advantage of such high-volume data to drive sales and user experience. In this special issue we will explore approaches for search, recommendations, business analytics, computational advertising, and other related aspects of Information Discovery in the eCommerce domain. The task is superficially the same as web-page search (fulfill a user's information need), but how this is achieved is very much different. On leading eCommerce websites (such as eBay, Flipkart, Amazon, and Alibaba), the traditional web-page ranking features are either not present or are present in a very different form. The entities that need to be discovered (the information that fulfills the need) might be unstructured, associated with structure, semi-structured, or have facets such as: price, ratings, title, description, seller location, and so on. Domains with such facets raise interesting research challenges such as a) relevance and ranking functions that take into account the tradeoffs across various facets with respect to the input query b) recommendations based on entity similarity, user location (e.g. shipping cost). These challenges require an inherent understanding of product attributes, user behavior, and the query context. Unlike document and web search, product sites are also characterized by the presence of a dynamic inventory with a high rate of change and turnover, and a long tail of query distribution. Outside of search but still within Information Retrieval, the same feature in different domains can have radically different meaning. For example, in email filtering the presence of “Ray-Ban” along with a price is a strong indication of spam, but within an auction setting this likely indicates a valid product for sale. Another example is natural language translation; company names, product names, and even product descriptions do not translate well with existing tools. Similar problems exist with knowledge graphs that are not customized to match the product domain. In addition to the above topics, this special issue will also focus on AI and machine learning enhanced business analytics approaches for understanding online shopping and consumer behaviors. Another area of focus is computational modeling and analysis of advertising and other promotional forms in eCommerce. The main objective of this special issue is to publish an up-to-date high-quality set of papers that deal with AI driven information discovery in the eCommerce domain. All journal submissions will be reviewed by at least three reviewers recruited by the editors of the special issue. We expect to accept about 10 papers. The special issue relates to all aspects of eCommerce search and recommendations. Research topics and challenges that are usually encountered in this domain include: Machine learning techniques such as online learning and deep learning for eCommerce applications Semantic representation for users, products, and services & Semantic understanding of queries Structured data and faceted search, for example, converting unstructured data to its structured form The use of domain specific facets in search and other IR tasks, and how those facets are chosen Counterfactual learning Query intent, suggestion, and auto-completion Temporal dynamics for Search and Recommendation Models for relevance and ranking for multi-faceted entities Recall-oriented search for eCommerce including deterministic sorting of results lists (e.g. price low to high) Click models for eCommerce domain Session aware, and session-oriented search and recommendation Construction and use of knowledge graph, and ontologies for search and recommendations Personalization & contextualization, and the use of personal facets such as age, gender, location etc. Indexing and search in a rapidly changing environment (for example, an auction site) Efficiency and scalability Diversity in product search and recommendations Strategies for resolving extremely low (or no) recall queries The use of external features such as reviews and ratings in ranking User interfaces (mobile, desktop, voice, etc.) and personalization Reviews and sentiment analysis The use of social signals in ranking and beyond The balance between business requirements and user requirements (revenue vs relevance) Trust and security Live experimentation Questions and answering, chat bots for eCommerce Cross-Lingual search and machine translation Fashion eCommerce Resources and data sets Computational advertising Display advertising Sponsored search advertising Keyword advertising Social advertising Real-time bidding Recommender systems Advertising personalization Advertising decisions and strategy optimization Advertising retrieval Ecommerce Analytics Real-time recommendation Big data analytics in eCommerce Predictive Analytics for eCommerce Retail Analytics in eCommerce
Last updated by Dou Sun in 2019-07-27
Special Issue on New Techniques in Media Quality Modeling
Submission Date: 2019-12-01

With the deployment of low-cost sensors, social media platforms, and cloud storage, the tremendous amount of image, video, and textual signals are cheaply available. As a standard tool to analyze these data, quality model has been pervasively used in domains like intelligent systems and 3D rendering. In the past decades, many shallow quality models have been released and commercialized. Despite their success, conventional quality models might be deficiently effective to handle the massive-scale data nowadays. Potential challenges include (not limited to): First, owing to the significant progress in deep feature engineering, deep quality models have been proposed and satisfactory performance was received. But deep model is conducted in a black-box manner, how to make it interpretable or transparent to quality modeling, and encoding human subjective wills and perception are still unsolved. Second, compared to the fully-annotated signals when modeling small-scale data, it is infeasible to label large-scale image/video at pixel-level due to the unaffordable human resources. In practice, only image/video-level labels or partial labels are available. Even worse, sometimes these weak labels are contaminated. Therefore, how to design a noiserobust weakly-supervised learning algorithm to exploring pixel-level quality-related elements is a tough problem. Third, conventional quality models typically leverage local/global features to evaluate each image/video, where human visual perception cannot be encoded explicitly. Apparently, human visual perception plays a significant role in quality modeling. In the literature, it is difficult to mimic human visual perception, i.e., predicting human gaze behavior and subsequently modeling the visual signal cognition in human brain. In this special issue, we will focus on the recent progress in image/video/text quality modeling and analytics. We aim to explore interpretable, noise-tolerant, and perceptionaware deep models to enhance quality models. Submissions related to new image/video/text benchmarks for testing the performance of quality models are also welcome. The primary objective for this special issue is to foster focused attention on the latest research progress in this cutting-edge area. We intend to attract researchers and practitioners from both industry and academia. Topics of interest include (but are not limited to): o New deep architectures for image/video quality evaluation; o Deep algorithms for enhancing the shallow-feature-based intelligent systems; o Quality-driven image/video processing techniques; o New Quality models in management applications; o Semantic models for deep image/video quality prediction; o New management tools based on deep quality models; o New machine learning algorithms for deep media quality modeling; o Visual quality prediction for photo and video management systems; o Leveraging human interactions to improve deep quality models; o Perception-aware quality models for Internet-scale media retrieval; o Novel deep quality features and their applications in pattern recognition. o Deep models trained using small samples for quality understanding; o Novel photo or video retargeting/cropping/re-composition using deep features; o New datasets, benchmarks, and validation of deep quality models; o Subjective methodologies to estimate the quality in real-world systems; o Novel visualization technologies for deep quality features;
Last updated by Dou Sun in 2019-06-28
Special Issue on Dark Side of Online Information Behavior
Submission Date: 2020-01-31

The dark side of online information behavior represents the negative phenomena associated with the management of information in the online environment. With the widespread availability of Internet and the emerging technologies, cyberspace becomes one of the most important channels for people to generate, organize, store, retrieve, acquire, disseminate and utilize information. Recognizing that information can be easily managed online although it causes different types of negative consequences. For example, 87 million Facebook user profiles have been improperly shared and misused by Cambridge Analytica, and online information privacy becomes a worldwide concern in recent years. Online fake news also exerts profound influence on political, economic, and social well-being. With the increasing volume of available information, we also witnessed a society of information overload and information anxiety. At the same time, information violence and harassment foster a hostile online environment. The power of artificial intelligence makes it easier for people to access the information they need, but it also creates information cocoons. Although there are many dark sides of online information behavior, current studies on this topic are still limited, leaving considerable gaps in the literature, particularly on how to conceptualize and operationalize the dark or unexpected negative sides of online information behaviors, how to theorize the underlying cognitive, psychological and social processes of such behaviors, and how to implement system design and information recognition to avoid negative information behaviors. The objective of this special issue thus is to push the boundaries of information behavior research, and draw the urgent attention of academics and practitioners to this important and fertile area. We believe this is a topic of challenges faced by multidisciplinary fields such as information systems, library and information science, computer science, marketing, communication and cognitive sciences. This special issue seeks high-quality and original contributions that advance the concepts, methods and theories by exploring the dark side of online information behaviors, and address the mechanisms, strategies and techniques for behavioral interventions. All contributions should clearly address the knowledge gaps indicated in the literature and will be peer-reviewed by the panel of experts associated with relevant field. This special issue is open to submissions from all theoretical and methodological perspectives. We particularly welcome research that challenges the boundaries of traditional academic thinking, integrating and expanding the knowledge rooted in diverse disciplines and within diverse contexts, and comes up with innovative ideas in theorizing and resolving the negative issues related to online information behavior. The topics of interest include, but are not limited to: Misinformation, disinformation and online fake news Information addiction, overload and underload Information privacy and security concerns Technophobia and information anxiety Information violence and harassment Illegal or unethical information searching, distribution and use Deceptive online communication Information cocoons and echo-chambers Information distractions, disruptions and interruptions Counterproductive online information behaviors Data-driven negative information extraction, recognition and validation methods System design that tracks and solves the above negative issues related to information behavior
Last updated by Dou Sun in 2019-08-11
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