مجله تعامل انسان و اطلاعات
سال هشتم شماره 2 (تابستان 1400)

The purpose of this study is to evaluate the University of Tehran website based on information architecture indicators to inform, assess the status and quality of the website.

The research method is applied descriptive. Checklist was used to analyze the website in terms of organization, labeling, navigation, and search systems.

The results of the checklists showed that the University of Tehran website earned 20 points out of 37 in the organization, 37 points out of 57 in the labeling, 53 points out of 78 in the navigation, and 14 points out of 46 in the search, which imply a poor status in search system, an average status in organization system, and a good status in labeling and navigation systems. According to the obtained results, the search system of the University of Tehran website can be redesigned, its organization system can be reviewed, and other mentioned systems can be improved. In general, in order to increase the efficiency of websites, it is necessary to consider the principles of information architecture in their design. Moreover, this research can be a suitable source for future website policies by identifying the strengths and weaknesses of the website.

Evaluating the information architecture of a website is a new field that, while important, has received less attention. This research tries to show the applicability of the method for use in similar cases by presenting a systematic evaluation in a case study for the University of Tehran website while introducing the strengths and weaknesses of this website.

The study aims to develop and validate a model for analyzing the textual feedback of users in social networks in the face of environmental events and actions with emphasis on identifying the factors affecting the presentation of text messages by users in social networks.
Research

Heuristic mixed method has been used. In the first stage, the meta-combined method was applied with a qualitative basis. In the second stage, to inspect, validate the identified factors and prepare the final research model, the survey method via questionnaire and forming conveyor group was combined. Population consisted of: 1) Selection and analysis of written documents related to the analysis of textual feedback and users' feelings, including 60 articles and works based on valid criteria from among 198 articles and works; 2) Experts in  information technology, sociology, behaviorism, etc., which 15 people were selected, but as a result and limitations of the corona pandemic comments and suggestions were remotely submitted.

Using the seven steps of meta-combination, a conceptual pattern was obtained in six conceptual layers, categories and codes. In each layer, concepts and topics were included, and to end 27 components were identified. For qualitative validation, the obtained model was found based on the opinions of experts in the form of focus groups and the conceptual model was approved by the research experts.

The conceptual model - obtained from the hybrid stages and focus groups – which has been approved and accepted by experts could be used as a basis for future research to guide, and direct the behavior of users in social  networking in order to provide strategies and executive policies for officials and decision makers in relevant organizations and institutions.

The aim of present study is the inquiring the environmental, technical and technological factors effective on knowledge hiding in scientific space.

The study is a mixed approach, inquiring the environmental, technical and technological factors effective on knowledge hiding in scientific space.  Thus, this article include in the pluralism of information collection methods. Also, using the "Delphi" method, the conceptual model of Inquiring the environmental, technical and technological factors effective on knowledge hiding in scientific space.
In quantitative terms, the sample was 314 employees of Islamic Azad University of Khorasan Razavi. Sample selected randomly and surveyed through standard knowledge hiding (Demirkasimoglu, 2016) and researcher-made environmental, technical and technological factors questionnaires. Data was collected during the first six months of 2020 and analyzed, using SEM and Amos software. Technical and technological factors include: Ease of accessibility, compliance of the organization's technology, fear of working with the existing technology in the organization and satisfaction with quality, and for environmental factors, the physical environment and social environment were considered.

Results show that the effect of physical and social environment on knowledge concealment with the standard coefficients of -0.70 and -0.39, respectively. Also, the amount of effectiveness of ease of accessibility, the factor of compliance with the organization's technology, the factor of fear and apprehension of working with existing technology in the organization, the factor of quality satisfaction with knowledge concealment are equal to -0.65, -0.21 and – 0.45. But the fear variable has a direct effect of 0.96 by hiding knowledge.

Results indicate that: Environmental, technical and technological factors are inversely and significantly related to knowledge hiding.

To investigate the status of personal knowledge management skills of graduate students of Shahid Chamran University of Ahvaz and the effect of technological factors on improving personal knowledge management skills from the perspective of graduate students of Shahid Chamran University of Ahvaz.

This is a survey research method in which the ability to generalize the results is one of the most important advantages. The study investigates the effect of technological factor on personal knowledge management skills.

The results showed that graduate students at Shahid Chamran University are in good condition in terms of personal knowledge management skills. The technological  factors is above average. Also, technological factors are effective in improving personal knowledge management skills among students at Shahid Chamran University in Ahvaz. By upgrading one unit of technological factors, personal knowledge management skills will increase by 2,649 units.

Individual factors such as knowledge and experience, personality and psychological characteristics, the ability to communicate with others and to use technology are among the factors that affect the management of personal knowledge. In addition, organizational facilities and the culture that governs the organization also affect the management of personal knowledge. The situation of graduate students at Shahid Chamran University is in good condition in terms of personal knowledge management skills. The average factors is above normal. It can be accepted with 95% confidence that the personal knowledge management skills state of graduate students at Shahid Chamran University is appropriate and at the anticipated level, technological factors are effective in improving personal knowledge management skills among students at Shahid Chamran University.

Scientific article recommender system assists and advance information retrieval process by proposing and offering articles tailored to the researchers needs. The main purpose of this study is to evaluate the performance of the recommender System in three scientific databases.  

This applied study is directed by the valuation method. Sample consisted of three scientific databases: Elsevier, Taylor & Francis, and Google Scholar, which share recommendation tools. "Information storage and retrieval" was selected as the search subject. Ten specialized keywords related to the topic of information storage and retrieval were selected. After searching each key words, the first retrieved article was reviewed. Then, for each first article, the first 5 recommended articles were mined in each of the three mentioned databases. Data was collected through direct observation using a researcher-made checklist. To evaluate subject relevance, bibliographic information of the first article retrieved in each subject and database along with the bibliographic information of 5 recommended articles was provided to two groups of librarians and IT professionals. Sample was selected by snowball method. Descriptive and inferential statistics were used to analyze the data.

Findings showed that among the databases, Elsevier recommends more relevant results from the perspective of IT professionals and librarians in the field of information storage and retrieval, with Google Scholar and Taylor & Francis in the next ranks. In total, the most relevant articles in terms of subject experts were the articles that ranked fifth.

To sum up, Elsevier performed better than the other two databases in terms of recommending related articles. Also, there is a significant difference between the views of librarians and IT professionals regarding the relevance of recommended articles in the field of information storage and retrieval. Thus, from the point of view of IT professionals, the significance of the recommended articles is greater.

The meaning of cross-domain recommendation is that instead of dealing with each domain independently, transfer knowledge gained in one domain (source) to another domain (target) and use it. The present article systematically reviews the research in this field in terms of foundations, applications and challenges.

The Prisma guidelines had been used. Search in Persian and English scientific information sources with related keywords were conducted and 98 English language sources were found in the period 2007 to 2021. Applying the initial refinement, inclusion and exclusion criteria by experts, 28 English documents were selected to enter in the systematic review.

There are four levels of cross-domain recommendations: Attributes, types, items and systems. Machine learning algorithms are used to predict user rating in cross-domain recommendations, and three categories of:  Prediction, ranking, and classification criteria are used to evaluate predictions based on confusion matrix. Cross-domain recommendations can be used to increase the accuracy of recommendations, resolve cold start problems, cross-sell, and improve personalization by transferring knowledge between domains. The most challengeable recommendations of cross-domain is the differences between domains. These differences include the mismatch between the properties of the domains and/or unclear relationships between the domains. In addition, differences in domain size and poor performance of basic algorithms in predicting user rating are other challenges in cross-domain recommendations.

While this subject has been shaped in the last decade, but the keen attention of computer science and information researchers shows its importance. Items level are the main category of cross-domain recommendations. Due to the formation of e-business groups, in the future, cross-domain recommendations at the system level will be given more consideration. Cross-domain recommendations could be used to improve the performance of recommender systems, user modeling in human-computer interaction, and e-commerce.