Reduce Information Overload Research Articles

A Mobile Service Recommendation System Using Multi-Criteria Ratings

With the rapid advancement of wireless technologies and mobile devices, mobile services offer great convenience and huge opportunities for service creation. However, information overload make service recommendation become a crucial issue in mobile services. Although traditional single-criteria recommendation systems have been successful in a number of personalization applications, obviously individual criterion cannot satisfy consumers’ demands. Relying on multi-criteria ratings, this paper presents a novel recommendation system using the multi-agent technology. In this system, the ratings with respect to the three criteria are aggregated into an overall service ranking list by a rank aggregation algorithm. Furthermore, all of the services are classified into several clusters to reduce information overload further. Finally, Based on multi-criteria rank aggregation, the prototype of a recommendation system is implemented. Successful applications of this recommendation system have demonstrated the efficiency of the proposed approach.

Personalizing map content to improve task completion efficiency

Significant interaction challenges arise in both developing and using interactive map applications. Users encounter problems of information overload in using interactive maps to complete tasks. This is further exacerbated by device limitations and interaction constraints in increasingly popular mobile platforms. Application developers must then address restrictions related to screen size and limited bandwidth in order to effectively display maps on mobile devices. In order to address issues of user information overload and application efficiency in interactive map applications, we have developed a novel approach for delivering personalized vector maps. Ongoing task interactions between users and maps are monitored and captured implicitly in order to infer individual and group preferences related to specific map feature content. Personalized interactive maps that contain spatial feature content tailored specifically to users' individual preferences are then generated. Our approach addresses spatial information overload by providing only the map information necessary and sufficient to suit user interaction preferences, thus simplifying the completion of tasks performed with interactive maps. In turn, tailoring map content to specific user preferences considerably reduces the size of vector data sets necessary to transmit and render maps on mobile devices. We have developed a geographic information system prototype, MAPPER (MAP PERsonalization), that implements our approach. Experimental evaluations show that the use of personalized maps helps users complete their tasks more efficiently and can reduce information overload.

Novel Representation of Clinical Information in the ICU

The introduction of electronic medical records (EMR) and computerized physician order entry (CPOE) into the intensive care unit (ICU) is transforming the way health care providers currently work. The challenge facing developers of EMR's is to create products which add value to systems of health care delivery. As EMR's become more prevalent, the potential impact they have on the quality and safety, both negative and positive, will be amplified. In this paper we outline the key barriers to effective use of EMR and describe the methodology, using a worked example of the output. AWARE (Ambient Warning and Response Evaluation), is a physician led, electronic-environment enhancement program in an academic, tertiary care institution's ICU. The development process is focused on reducing information overload, improving efficiency and eliminating medical error in the ICU.

Visualizing the drug target landscape

Generating new therapeutic hypotheses for human disease requires the analysis and interpretation of many different experimental datasets. Assembling a holistic picture of the current landscape of drug discovery activity remains a challenge, however, because of the lack of integration between biological, chemical and clinical resources. Although tools designed to tackle the interpretation of individual data types are abundant, systems that bring together multiple elements to directly enable decision making within drug discovery programmes are rare. In this article, we review the path that led to the development of a knowledge system to tackle this problem within our organization and highlight the influences of existing technologies on its development. Central to our approach is the use of visualization to better convey the overall meaning of an integrated set of data including disease association, druggability, competitor intelligence, genomics and text mining. Organizing such data along lines of therapeutic precedence creates clearly distinct 'zones' of pharmaceutical opportunity, ranging from small-molecule repurposing to biotherapeutic prospects and gene family exploitation. Mapping content in this way also provides a visual alerting mechanism that evaluates new evidence in the context of old, reducing information overload by filtering redundant information. In addition, we argue the need for more tools in this space and highlight the role that data standards, new technologies and increased collaboration might have in achieving this aim.

Commentary: IDSA Guidelines for Improving the Teaching of Preclinical Medical Microbiology and Infectious Diseases

Preclinical microbiology and infectious diseases courses too often primarily depend on PowerPoint lectures and notes, combined with multiple-choice tests, as their primary teaching tools. This strategy sets low expectations for students, encouraging short-term memory and discouraging understanding and long-term memory. These methods also fail to stimulate active participation, collaborative learning, and two-way communication with the professor, and they do not respect the students' diverse talents and ways of learning. The Infectious Diseases Society of America Preclinical Curriculum Committee proposes a new approach that emphasizes active learning and understanding and that addresses all of these failures. It consists of five components: (1) "Just-in-time" teaching that requires students to e-mail the answers to two general questions as well as any areas of misunderstanding to the instructor several hours before each lecture, (2) peer instruction or large-group sessions consisting of student teams of four who electronically answer a conceptual question before each major section of the lecture, (3) teaching from edited textbooks and Internet sources, (4) small-group discussions that emphasize pathogenesis and differential diagnosis, and (5) essay questions that encourage and test understanding in addition to recognition. A national consensus on factual content is proposed, with the goals of reducing information overload and minimizing requirements for excessive memorization. These strategies promise to enhance learning and rekindle interest in the field of infectious diseases. Other subspecialty organizations should create similar teaching guidelines that will encourage future medical students to bring a richer understanding of clinical and basic science to the bedside.

An investigation of student practices in asynchronous computer conferencing courses

This study investigated the online practices of students enrolled in graduate-level distance education courses. Using interviews and a questionnaire as data sources, the study sought to: (a) identify common practices that students adopt in asynchronous discussions, and (b) gain an understanding of why students adopt them. An analysis of the data suggests that many of the practices are coping mechanisms developed to help students more easily meet course participation requirements. Some of these are time saving strategies designed to reduce information overload (e.g., skimming messages rather than reading them carefully). Other strategies are designed to help students project an image of themselves as knowledgeable and collaborative course participants. It is argued that although these practices provide students with a level of efficiency in terms of meeting course requirements, they may inadvertently undermine learning.

Program transformations for information personalization

Personalization constitutes the mechanisms necessary to automatically customize information content, structure, and presentation to the end-user to reduce information overload. Unlike traditional approaches to personalization, the central theme of our approach is to model a website as a program and conduct website transformation for personalization by program transformation (e.g., partial evaluation, program slicing). The goal of this paper is study personalization through a program transformation lens, and develop a formal model, based on program transformations, for personalized interaction with hierarchical hypermedia. The specific research issues addressed involve identifying and developing program representations and transformations suitable for classes of hierarchical hypermedia, and providing supplemental interactions for improving the personalized experience. The primary form of personalization discussed is out-of-turn interaction—a technique which empowers a user navigating a hierarchical website to postpone clicking on any of the hyperlinks presented on the current page and, instead, communicate the label of a hyperlink nested deeper in the hierarchy. When the user supplies out-of-turn input we personalize the hierarchy to reflect the user's informational need. While viewing a website as a program and site transformation as program transformation is non-traditional, it offers a new way of thinking about personalized interaction, especially with hierarchical hypermedia. Our use of program transformations casts personalization in a formal setting and provides a systematic and implementation-neutral approach to designing systems. Moreover, this approach helped connect our work to human-computer dialog management and, in particular, mixed-initiative interaction. Putting personalized web interaction on a fundamentally different landscape gave birth to this new line of research. Relating concepts in the web domain (e.g., sites, interactions) to notions in the program-theoretic domain (e.g., programs, transformations) constitutes the creativity in this work.

Improving Social Recommender Systems

Recommender systems play a significant role in reducing information overload for people visiting online sites, but their accuracy could be improved by using data from online social networks and electronic communication tools.

P.1.g.009 Attentional bias towards health-threat information in chronic fatigue syndrome

Despite researchers has devoted to understanding the impacts of manager response on the helpfulness of online review, the underlying mechanism of how the topic consistency and linguistic style similarity between manager response and corresponding review may influence review helpfulness remains unclear. Based on Elaboration Likelihood Model, a research model reflected such effects on online review helpfulness was developed and empirical examined against 51,627 consumer reviews collected from Tripadvisor.com. Specifically, the moderating effects of topic consistency and linguistic style similarity between manager response and consumer review on the relationships between two important cues (text sentiment and review length) and review helpfulness were tested. The results of negative binomial regression showed that text sentiment as the central cue has a negative effect on review helpfulness, while review length as the peripheral cue has a positive effect on review helpfulness. As expected, the negative influences of text sentiment on review helpfulness will be weakened when the topic consistency level is high or when the linguistic style similarity level is high. The positive influences of review length on review helpfulness will also be weakened when topic consistency or linguistic style similarity are at its high level. This study thus can not only help retailers identify useful reviews, but also help potential consumers reduce information overload.

Information overload: what's behind it, what's beyond it?

Literature alert services, good evidence-based resources and real-time decision support can all help to reduce information overload.

An Laboratory Experiment for Comparing Effectiveness of Three Types of Online Recommendations

The widespread use of Internet accelerates the rapid development of business to customer electronic commerce. To reduce information overload and help their customers to make better purchase decisions, e-commerce websites are beginning to use online recommendations. This paper compares the effectiveness of three types of online recommendations, the personalized recommendation, best sellers, and consumers' reviews, which are widely used in e-commerce. This research used a laboratory experiment combined with a questionnaire. This paper also establishes an integrated model of the facts that influence recommendation effectiveness.

Online shopping recommendation mechanism and its influence on consumer decisions and behaviors: A causal map approach

Purpose of this paper: Online product recommendation mechanism (agents) are becoming increasingly available on websites to assist consumers with reducing information overload, provide advice in finding suitable products, and facilitate online consumer decision-making. Central of these services is consumers' satisfaction with recommendation results. Traditional recommendation mechanism (TRM) is based content and/or collaborative filtering approach. However, the remaining problem concerning TRM is how to analyze the causal relationships between quantitative and qualitative factors, and investigate their impact on the central routes and peripheral routes through which both quantitative and qualitative factors can affect customer online shopping decisions. It is well known that qualitative factors are hard to codify yet they have a significant effect on a customer's decision-making process in the form of causal relationships with quantitative factors. Thus, a new online recommendation mechanism is required that incorporates qualitative factors systematically with quantitative factors to analyze their combined influence on customers' purchasing decision-making process. So, our study suggest that causal maps based recommendation mechanism (CMRM). Design/methodology/approach: ELM was applied to build hypotheses concerning how consumers' decision satisfaction and online shopping behavior are affected by CMRM. Specifically, the performance of the proposed CMRM is analyzed empirically by garnering the experiment data from 250 qualified respondents who were asked to refer to the proposed CMRM before making purchasing decisions on mobile phones. Findings: Statistical results proved that the proposed CMRM could enhance consumers' decision satisfaction, attitude towards the recommended products, as well as positive purchase intentions and actual purchase. Practical implications: CMRM can be easily implemented on the web, allowing target consumers to experience a real recommendation process. And, a wide variety of qualitative factors that seem crucial to most consumers can be pre-defined through a survey, and incorporated into causal maps. Thus, such causal maps will improve the personalization effect on the target consumer's purchase intentions.

Model-directed Web transactions under constrained modalities

Online transactions (e.g., buying a book on the Web) typically involve a number of steps spanning several pages. Conducting such transactions under constrained interaction modalities as exemplified by small screen handhelds or interactive speech interfaces—the primary mode of communication for visually impaired individuals—is a strenuous, fatigue-inducing activity. But usually one needs to browse only a small fragment of a Web page to perform a transactional step such as a form fillout, selecting an item from a search results list, and so on. We exploit this observation to develop an automata-based process model that delivers only the “relevant” page fragments at each transactional step, thereby reducing information overload on such narrow interaction bandwidths. We realize this model by coupling techniques from content analysis of Web documents, automata learning and statistical classification. The process model and associated techniques have been incorporated into Guide-O, a prototype system that facilitates online transactions using speech/keyboard interface (Guide-O-Speech), or with limited-display size handhelds (Guide-O-Mobile). Performance of Guide-O and its user experience are reported.

Fuzzy collaborative filtering with multiple agents

Automated collaborative filtering has become a popular technique for reducing information overload. We have developed a new method for recommending items using multiple agents. The agents were established by employing the fuzzy C-means clustering technique. We employ these agents collaborating each other to get recommendation for users. The results were evaluated by using MovieLens movie’s rating data. It is shown that the algorithm is an effective metrics in collaborative filtering.

Perceptions of Integrated Electronic Marine Systems – A Preliminary Study

Integrated electronic marine systems have been put forward as one way of reducing information overload for officers of the watch and other personnel who work on ships' bridges or in fishing wheelhouses. It may be that such systems will improve safety records which, in the case of fishing, are amongst the worst in the world. This article builds on previous work about issues for the systems designer by using a small study of fishing skippers to ascertain their perceptions of using integrated electronic systems in the wheelhouse. Semi-structured interviews were used with 11 skippers from one port who were all familiar with integrated electronic marine systems but did not use them on their vessels. The main reasons for this were a perception of unreliability of both the hardware and the software, together with concerns about data integrity and user control.

逐次学習を伴う線形ファジィクラスタリングによる適応的な協調フィルタリング

Collaborative filtering is a technique for reducing information overload, and personalized recommendation is performed by predicting missing values in a data matrix. Linear fuzzy clustering is a technique for local principal component analysis and can be used for constructing local prediction models considering data substructures. This paper proposes a new algorithm for constructing local linear models that performs a simultaneous application of fuzzy clustering and principal component analysis based on sequential subspace learning. In numerical experiments, the diagnostic power of the filtering system is shown to be improved by predicting missing values using the proposed local linear models.

A user-oriented contents recommendation system in peer-to-peer architecture

The pervasive deployment of P2P (peer-to-peer) systems and the multimedia contents overload in web environment raise a serious complexity for the peers where peers that participate in a P2P network are no longer able to effectively choose the contents they want. Recommender systems have been popularly used for reducing information overload of internet surfers by suggesting products or digital contents that are most valuable for them. But most existing recommender systems have been worked in client–server architecture. This paper proposes a PEOR (PEer-ORiented Recommender system), a collaborative filtering-based multimedia contents recommender system in P2P architecture, to obtain the peers’ search efficiency. To adopt a change in peer preferences PEOR uses recent ratings of peers for recommendations, thereby leading to better quality recommendations. And to enhance the system performance, PEOR searches for nearest peers with similar preference through peer-based local information only. We implemented the system and evaluated its performance with real transaction data in S content provider offering character images. Our experimental data shows that PEOR offers not only remarkably higher quality of recommendations but also the dramatically faster performance than the centralized benchmark system.

Buyer agent to enhance consumer awareness: SAATHI

Personal agents have been developed that assist a user with information processing needs by generating, filtering, collecting, or transforming information. On the other hand, internet stores are providing services customized by the needs and interests of individual customers. Such services can be viewed as “seller’s agents” whose goal is to push merchandise and/or services on to the users. This leads us to believe that there is a growing need for deploying “buyer’s agents” whose goal is to best serve the consumer’s interests. The Internet contains a huge volume of information which can overwhelm a buyer. The buyers may often make misinformed decisions based on partial, outdated, irrelevant or incorrect information. We have identified several key functionalities of buyer’s agents whose goal is to reduce information overload and improve relevancy and accuracy of information for consumers. In particular, such agents can make consumers aware of complex interactions between specified preferences and prevailing market conditions, provide differential analysis for decision support, and use domain ontologies to help the user reformulate queries to improve satisfaction with query results. We present SAATHI, a prototype buyer’s agent that demonstrate some of these functionalities in an apartment locator domain.

Integrated Marine Electronic Systems – Some User Associated Issues for the Designer

Marine accidents have many causes but a recurring theme is poor watch-keeping often caused by weak bridge management. Information overload is sometimes blamed for accidents and attempts to reduce information overload may include electronic systems which have been produced for integrating information from various electronic sources so that information is concentrated on fewer screens. This article explores some of the issues facing the designers of such systems. While acknowledgement is given to some of the technical problems, the article concentrates on those issues associated with the user of such systems, in particular, domain knowledge, screen design and user control. Available Marine Accident Investigation Reports for 2004 are used to highlight that, in that year, only one passenger vessel incident was attributed in part to poor data from electronic aids. However, it is accepted that a much wider study of such reports is needed before any firm conclusions can be drawn.

Havana agents for comparison shopping and location-aware advertising in wireless mobile environments

Users of handheld wireless devices do not have the luxury to wade through pages and pages of information to find the products and services they need. In addition, the inconveniences of using the thumb controls on a cellular phone to navigate, along with the high costs of wireless data network access highlights the problems that wireless users face. The need for direct access to highly relevant, location-aware information with a minimal amount of browsing and a minimal amount of user input is what prompted us to investigate the feasibility of and development of a plan for deploying mobile agents to perform information filtering and retrieval on behalf of the users in a mobile commerce setting. In this paper, we discuss a novel mobile agent platform that can be used for comparison shopping in a mobile wireless environment, through which businesses can better understand and communicate with the mobile consumer. The proposed agent-based approach helps the end user reduce information overload by helping her filter out unnecessary information and only show information that is relevant; it provides novel ways for cellular service providers to compete, and businesses to acquire new customers. We will address the challenges associated with such an evolutionary approach, and provide some sample scenarios, along with an architectural design for the proposed system. A proof-of-concept prototype using Java RMI has been constructed as part of the Havana mobile agent platform.