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Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

Systems to support literature review (LR) and manuscript preparation tend to focus on only one or two of the tasks involved. The purpose of this paper is to describe an intervention framework that redesigns a particular set of tasks, allowing for interconnectivity between the tasks and providing appropriate user interface display features for each task in a prototype system.

A user evaluation study was conducted on the prototype system. The system supports the three tasks: building a reading list (RL) of research papers, finding similar papers based on a set of papers and shortlisting papers from the final RL for inclusion in manuscript based on article type. A total of 119 researchers who had experience in authoring research papers, participated in the evaluation study. They had to select one of the provided 43 topics and execute the tasks offered by the system. Three questionnaires were provided for evaluating the tasks and system. Both quantitative and qualitative analyses were performed on the collected evaluation data.

Task redesign aspects had a positive impact in user evaluation for the second task of finding similar papers while improvement was found to be required for the first and third tasks. The tasks interconnectivity features seed basket and RL were helpful for the participants in conveniently searching for papers within the system. Two of the four proposed informational display features, namely, information cue labels and shared co-relations were the most preferred features of the system. Student user group found the task recommendations and the overall system to be more useful and effective than the staff group.

This study validates the importance of interconnected task design and novel informational display features in accentuating task-based recommendations for LR and manuscript preparatory tasks. The potential for improvement in recommendations was shown through the task redesign exercise where new requirements for the tasks were identified. The resultant prototype system helps in bridging the gap between novices and experts in terms of LR skills.

During the literature review phase, the task of finding similar research papers can be a difficult proposition for researchers due to the procedural complexity of the task. Current systems and approaches help in finding similar papers for a given paper, even though researchers tend to additionally search using a set of papers. This paper aims to focus on conceptualizing and developing recommendation techniques for key literature review and manuscript preparatory tasks that are interconnected. In this paper, the user evaluation results of the task where seed basket-based discovery of papers is performed are presented.

A user evaluation study was conducted on a corpus of papers extracted from the ACM Digital Library. Participants in the study included 121 researchers who had experience in authoring research papers. Participants, split into students and staff groups, had to select one of the provided 43 topics and run the tasks offered by the developed assistive system. A questionnaire was provided at the end of each task for evaluating the task performance.

The results show that the student group evaluated the task more favourably than the staff group, even though the difference was statistically significant for only 5 of the 16 measures. The measures topical relevance, interdisciplinarity, familiarity and usefulness were found to be significant predictors for user satisfaction in this task. A majority of the participants, who explicitly stated the need for assistance in finding similar papers, were satisfied with the recommended papers in the study.

The current research helps in bridging the gap between novices and experts in terms of literature review skills. The hybrid recommendation technique evaluated in this study highlights the effectiveness of combining the results of different approaches in finding similar papers.

Although many interventional approaches have been proposed to address the apparent gap between novices and experts for literature review (LR) search tasks, there have been very few approaches proposed for manuscript preparation (MP) related tasks. The purpose of this paper is to describe a task and an incumbent technique for shortlisting important and unique papers from the reading list (RL) of researchers, meant for citation in a manuscript.

A user evaluation study was conducted on the prototype system which was built for supporting the shortlisting papers (SP) task along with two other LR search tasks. A total of 119 researchers who had experience in authoring research papers participated in this study. An online questionnaire was provided to the participants for evaluating the task. Both quantitative and qualitative analyses were performed on the collected evaluation data.

Graduate research students prefer this task more than research and academic staff. The evaluation measures relevance, usefulness and certainty were identified as predictors for the output quality measure “good list”. The shortlisting feature and information cues were the preferred aspects while limited data set and rote steps in the study were ascertained as critical aspects from the qualitative feedback of the participants.

Findings point out that researchers are clearly interested in this novel task of SP from the final RL prepared during LR. This has implications for digital library, academic databases and reference management software where this task can be included to benefit researchers at the manuscript preparatory stage of the research lifecycle.

The scientific community shares a heritage of knowledge generated by several different fields of research. Identifying how scientific interest evolves is relevant for recording and understanding research trends and society’s demands.

This article presents SciBR-M, a novel method to identify scientific interest evolution from bibliographic material based on Formal Concept Analysis. The SciBR-M aims to describe the thematic evolution surrounding a field of research. The method begins by hierarchically organising sub-domains within the field of study to identify the themes that are more relevant. After this organisation, we apply a temporal analysis that extracts implication rules with minimal premises and a single conclusion, which are helpful to observe the evolution of scientific interest in a specific field of study. To analyse the results, we consider support, confidence, and lift metrics to evaluate the extracted implications.

The authors applied the SciBR-M method for the Educational Data Mining (EDM) field considering 23 years since the first publications. In the digital libraries context, SciBR-M allows the integration of the academy, education, and cultural memory, in relation to a study domain.

Cultural changes lead to the production of new knowledge and to the evolution of scientific interest. This knowledge is part of the scientific heritage of society and should be transmitted in a structured and organised form to future generations of scientists and the general public.

The method, based on Formal Concept Analysis, identifies the evolution of scientific interest to a field of study. SciBR-M hierarchically organises bibliographic material to different time periods and explores this hierarchy from proper implication rules. These rules permit identifying recurring themes, i.e. themes subset that received more attention from the scientific community during a specific period. Analysing these rules, it is possible to identify the temporal evolution of scientific interest in the field of study. This evolution is observed by the emergence, increase or decrease of interest in topics in the domain. The SciBR-M method can be used to register and analyse the scientific, cultural heritage of a field of study. In addition, the authors can use the method to stimulate the process of creating knowledge and innovation and encouraging the emergence of new research.

This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. To achieve this aim, the authors use systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected recommender systems and its main techniques, as well as their benefits and drawbacks in general.

In this paper, the SLR method is utilized with the aim of identifying, evaluating and integrating the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. Also, the authors discussed recommender system and its techniques in general without a specific domain.

The major developments in categories of recommender systems are reviewed, and new challenges are outlined. Furthermore, insights on the identification of open issues and guidelines for future research are provided. Also, this paper presents the systematical analysis of the recommender system literature from 2005. The authors identified 536 papers, which were reduced to 51 primary studies through the paper selection process.

This survey will directly support academics and practical professionals in their understanding of developments in recommender systems and its techniques.

With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations.

This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions.

As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction.

The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization.

A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.

The purpose of this paper is to present an empirical comparison between the recommendations generated by a citation-based recommender for research articles in a digital library with those produced by a user-based recommender (ExLibris “bX”).

For these computer experiments 9,453 articles were randomly selected from among 6.6 M articles in a digital library as starting points for generating recommendations. The same seed articles were used to generate recommendations in both recommender systems and the resulting recommendations were compared according to the “semantic distance” between the seed articles and the recommended ones, the coverage of the recommendations and the spread in publication dates between the seed and the resulting recommendations.

Out of the 9,453 test runs, the recommendation coverage was 30 per cent for the user-based recommender vs 24 per cent for the citation-based one. Only 12 per cent of seed articles produced recommendations with both recommenders and none of the recommended articles were the same. Both recommenders yielded recommendations with about the same semantic distance between the seed article and the recommended articles. The average differences between the publication dates of the recommended articles and the seed articles is dramatically greater for the citation-based recommender (+7.6 years) compared with the forward-looking user-based recommender.

This paper reports on the only known empirical comparison between the Ex Librix “bX” recommendation system and a citation-based collaborative recommendation system. It extends prior preliminary findings with a larger data set and with an analysis of the publication dates of recommendations for each system.

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

The aim of this paper is to investigate contextual information that has an impact on the process of selection and decision making in recommender systems (RSs) in digital libraries.

Using a grounded theory method of qualitative research, semi‐structured interviews were carried out with 22 information specialists, and IT and computer engineering students and professors. Data resulting from interviews were analysed in two stages using open coding, followed by axial and selective coding.

The central idea (concept) developed in this study, named scientific research ground (SRG), is an information ground users step into with scholarly purposes. Within SRG they start interacting with information systems. SRG has contexts which situate users in a range of situations while interacting with information systems. Users' characteristics such as purpose, activity, literacy, mental state, expectations, and assumptions, occupational and social status are some contexts that should be taken into account for making a recommendation.

This study sought to explore contextual information in the academic community and the academic contextual information cannot be generalized to RSs in other environments such as e‐commerce.

Identifying and implementing contextual information in information systems can help make better recommendations as well as improve interaction between users and information systems.

Based on the SRG idea and its contexts, a multi‐layer contextual model for a recommender system is proposed.