On the Notion of “An Information Need”

2009 ◽  
pp. 354-357 ◽  
Author(s):  
Eduard Hoenkamp
Keyword(s):  
Information Need

The Effect of Searching Versus Surfing on Cognitive and Emotional Responses to Online News

2009 ◽  
Vol 21 (2) ◽  
pp. 49-59 ◽  
Author(s):  
Kevin Wise ◽  
Hyo Jung Kim ◽  
Jeesum Kim
Keyword(s):  
Heart Rate ◽  
Skin Conductance ◽  
Skin Conductance Level ◽  
Emotional Responses ◽  
Online News ◽  
Self Report ◽  
Mixed Design ◽  
Information Need ◽  
News Stories ◽  
Rate Acceleration

A mixed-design experiment was conducted to explore differences between searching and surfing on cognitive and emotional responses to online news. Ninety-two participants read three unpleasant news stories from a website. Half of the participants acquired their stories by searching, meaning they had a previous information need in mind. The other half of the participants acquired their stories by surfing, with no previous information need in mind. Heart rate, skin conductance, and corrugator activation were collected as measures of resource allocation, motivational activation, and unpleasantness, respectively, while participants read each story. Self-report valence and recognition accuracy were also measured. Stories acquired by searching elicited greater heart rate acceleration, skin conductance level, and corrugator activation during reading. These stories were rated as more unpleasant, and their details were recognized more accurately than similar stories that were acquired by surfing. Implications of these results for understanding how people process online media are discussed.

PEMILIHAN MEDIA KOMUNIKASI DARI MAHASISWA UNIVERSITAS BUNDA MULIA ANGKATAN 2010/2011 JURUSAN MANAJEMEN

2017 ◽  
Vol 6 (2) ◽  
Author(s):  
Lelly Christin
Keyword(s):  
Market Share ◽  
Direct Mail ◽  
Communication Media ◽  
Marketing Communication ◽  
University Management ◽  
Integrated Marketing ◽  
Information Need ◽  
Integrated Marketing Communication ◽  
The Right ◽  
The University

<p>Lately competition becomes really tough, because of that each University has to choose the right strategy in order to increase their market share. Some of the strategies than can be done are by choosing the right communication media for each promotion that the university wants to do. For the reason, this research wants to know which communication media that really attracts students at Bunda Mulia University, Management Major in year 2010/2011. In this research, the writers used incidental sampling. The writers also use questionnaire for gathering the data or information need. To analyze the data, the writes use SPSS ver. 15.0 for windows. The conclusion of this research about the highest percentage to the lowest percentage of the most attractive communication media are television, internet, direct mail, magazines, radio, newspapers, outdor advertising, and the last one is telemarketing. According to result, the highest percentage of an attractive communication media is television, so writer suggest that the best media to do the promotion is television media.</p><p>Keyword :</p><p>Communication media, markeing communication, integrated marketing communication</p>

UGLEO: A WEB BASED INTELLIGENCE CHATBOT FOR STUDENT ADMISSION PORTAL USING MEGAHAL STYLE

2018 ◽  
Vol 23 (3) ◽  
pp. 175-191
Author(s):  
Anneke Annassia Putri Siswadi ◽  
Avinanta Tarigan
Keyword(s):  
Markov Chain ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Need ◽  
Web Based ◽  
Markov Chain Method ◽  
Information Center

To fulfill the prospective student's information need about student admission, Gunadarma University has already many kinds of services which are time limited, such as website, book, registration place, Media Information Center, and Question Answering’s website (UG-Pedia). It needs a service that can serve them anytime and anywhere. Therefore, this research is developing the UGLeo as a web based QA intelligence chatbot application for Gunadarma University's student admission portal. UGLeo is developed by MegaHal style which implements the Markov Chain method. In this research, there are some modifications in MegaHal style, those modifications are the structure of natural language processing and the structure of database. The accuracy of UGLeo reply is 65%. However, to increase the accuracy there are some improvements to be applied in UGLeo system, both improvement in natural language processing and improvement in MegaHal style.

Future of academic libraries: Opportunities and Challenges

2017 ◽  
Vol 4 (24) ◽  
Author(s):  
Raysh Thomas
Keyword(s):  
Academic Libraries ◽  
Search Engines ◽  
Information Seeking ◽  
Information Seeking Behavior ◽  
Technological Innovations ◽  
Information Need ◽  
Digital Resources ◽  
Web Resources ◽  
Seeking Behavior ◽  
High Bandwidth

Rapid advances in technological innovations, affordable high bandwidth networks, explosive growth of web resources,sophisticated search engines, ever growing digital resources and changing information seeking behavior of users are greatly transforming the future of academic libraries. The paper outlines the challenges which are very dominant and posing threat for the existence of academic libraries and suitable strategies requires to be made by the libraries and librarians to meet the expectations and information need of their existing and potential clienteles.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

Improving exploratory information retrieval for neophytes

2021 ◽  
Vol 20 (4) ◽  
pp. 50-64
Author(s):  
Bissan Audeh ◽  
Michel Beigbeder ◽  
Christine Largeron ◽  
Diana Ramírez-Cifuentes
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Digital Libraries ◽  
Specific Information ◽  
Information Need ◽  
External Resources ◽  
Learning Content ◽  
Citation Graph ◽  
Real Challenge ◽  
Access To Documents

Digital libraries have become an essential tool for researchers in all scientific domains. With almost unlimited storage capacities, current digital libraries hold a tremendous number of documents. Though some efforts have been made to facilitate access to documents relevant to a specific information need, such a task remains a real challenge for a new researcher. Indeed neophytes do not necessarily use appropriate keywords to express their information need and they might not be qualified enough to evaluate correctly the relevance of documents retrieved by the system. In this study, we suppose that to better meet the needs of neophytes, the information retrieval system in a digital library should take into consideration features other than content-based relevance. To test this hypothesis, we use machine learning methods and build new features from several metadata related to documents. More precisely, we propose to consider as features for machine learning: content-based scores, scores based on the citation graph and scores based on metadata extracted from external resources. As acquiring such features is not a trivial task, we analyze their usefulness and their capacity to detect relevant documents. Our analysis concludes that the use of these additional features improves the performance of the system for a neophyte. In fact, by adding the new features we find more documents suitable for neophytes within the results returned by the system than when using content-based features alone.

scholarly journals Herb–Drug Interactions: Worlds Intersect with the Patient at the Center

Medicines ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 44
Author(s):  
Mary Beth Babos ◽  
Michelle Heinan ◽  
Linda Redmond ◽  
Fareeha Moiz ◽  
Joao Victor Souza-Peres ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Case Report ◽  
Drug Interactions ◽  
Clinical Studies ◽  
Drug Information ◽  
Case Reports ◽  
Full Information ◽  
Information Need ◽  
Herbal Products ◽  
Reduce Risk

This review examines three bodies of literature related to herb–drug interactions: case reports, clinical studies, evaluations found in six drug interaction checking resources. The aim of the study is to examine the congruity of resources and to assess the degree to which case reports signal for further study. A qualitative review of case reports seeks to determine needs and perspectives of case report authors. Methods: Systematic search of Medline identified clinical studies and case reports of interacting herb–drug combinations. Interacting herb–drug pairs were searched in six drug interaction resources. Case reports were analyzed qualitatively for completeness and to identify underlying themes. Results: Ninety-nine case-report documents detailed 107 cases. Sixty-five clinical studies evaluated 93 mechanisms of interaction relevant to herbs reported in case studies, involving 30 different herbal products; 52.7% of these investigations offered evidence supporting reported reactions. Cohen’s kappa found no agreement between any interaction checker and case report corpus. Case reports often lacked full information. Need for further information, attitudes about herbs and herb use, and strategies to reduce risk from interaction were three primary themes in the case report corpus. Conclusions: Reliable herb–drug information is needed, including open and respectful discussion with patients.

scholarly journals Factors affecting breast cancer patients' need for genetic risk information: From information insufficiency to information need

2019 ◽  
Vol 28 (3) ◽  
pp. 543-557 ◽  
Author(s):  
Soo Jung Hong ◽  
Barbara Biesecker ◽  
Jennifer Ivanovich ◽  
Melody Goodman ◽  
Kimberly A. Kaphingst
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Genetic Risk ◽  
Risk Information ◽  
Breast Cancer Patients ◽  
Information Need ◽  
Factors Affecting ◽  
Genetic Risk Information
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