A Hypothesis is Placed to Justify the Extendibility of Recommender System/ Recommendation System into Social Life

Researchers still believe that the information filtering system/ collaborating system is a recommender system or a recommendation system. It is used to predict the "rating" or "preference" of a user to an item.  In other words, both predict rating or preference for an item or product on a specific platform. The aim of the paper is to extend the areas of the recommender system/recommendation systems. The basic task of the recommender system mainly is to predict or analyze items/product. If it is possible to include more products in the system, then obviously the system may be extended for other areas also. For example, Medicine is a product and doctors filter the particular medicine for the particular disease. In the medical diagnosis doctors prescribed a medicine and it a product. It depends on the disease of the user/patient so here doctor predicts a medicine or product just like an item is recommended in a recommender system. The main objective of the paper is to extend the Recommender System/Recommendation system in other fields so that the research works can be extended Social Science, Bio-medical Science and many other areas

A Network Resource Allocation Recommendation Method with An Improved Similarity Measure

Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail items. In this paper, to personalize the recommendations and allocate recommendation resources more purposively, a method named PIM+RA is proposed. This method utilizes a bipartite network that incorporates self-connecting edges and weights. Furthermore, an improved Pearson correlation coefficient is employed for better redistribution. The evaluation of PIM+RA demonstrates a significant enhancement not only in accuracy but also in coverage, diversity, and novelty of the recommendation. It leads to a better balance in recommendation frequency by providing effective exposure to long-tail items, while allowing customized parameters to adjust the recommendation list bias

A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach

Selection of a suitable Ph.D. supervisor is a very important step in a student’s career. This paper presents a multi-criteria decision support system to assist students in making this choice. The system employs a hybrid method that first utilizes a fuzzy analytic hierarchy process to extract the relative importance of the identified criteria and sub-criteria to consider when selecting a supervisor. Then, it applies an information retrieval-based similarity algorithm (TF/IDF or Okapi BM25) to retrieve relevant candidate supervisor profiles based on the student’s research interest. The selected profiles are then re-ranked based on other relevant factors chosen by the user, such as publication record, research grant record, and collaboration record. The ranking method evaluates the potential supervisors objectively based on various metrics that are defined in terms of detailed domain-specific knowledge, making part of the decision making automatic. In contrast with other existing works, this system does not require the professor’s involvement and no subjective measures are employed

RESEARCH ON INFORMATION RESOURCES AGGREGATION IN ACADEMIC TO SEMANTIC PUBLISHING

With the constant development of information and digitization, the proportion of digitization in scientific research publications is increasing day by day. On the one hand, the rapid growth of digital scientific research data and academic literature has provided many facilities for academic exchanges among scientific research users. On the basis of systematically combing the relevant theories of semantic publishing and information resource integration, this paper summarizes the current situation of information resource aggregation in academic journals and the significance of digital resource aggregation. Secondly, this paper illustrates the important role of semantic information resource integration in semantic publishing of academic journals. Taking Elsevier semantic publishingmodel as an example, it focuses on the resource query and resource utilization under semantic publishing. Final adoption with the comparison of web of science database and the analysis and evaluation of the results of resource aggregation verify the feasibility of the semantic based digitalresource aggregation method in the digital publication of academic journals.Keywords: Semantic Publishing; Semantic Web, Digital Resource, and Aggregation elsevi

UTILIZING THE POTENTIALS OF BIG DATA IN LIBRARY ENVIRONMENTS IN NIGERIAN FOR RECOMMENDER SERVICES

The big data revolution has gained global attention and initiated creative innovations in every field and libraries as engines of access to information have also been affected by this new trend. Libraries in this part of the world have not utilized the amazing potential of big data in library services. In this time, when various terms such as algorithms age, petabytes age, data age, etc. are been used to describe the activities initiated by machine learning, industries and organizations can achieve much by incorporating inspiring and innovative tools to improve services and performance. In this vein libraries in Nigeria are expected against all odds to make their services more interactive, attractive, innovative, and exciting by utilizing cloud technologies and machine learning techniques to create recommender services. This paper titled “Utilizing the Potentials of Big Data in Nigeria Library Environments by Recommender Services”, focuses on the concept and characteristics of big data and its importance in complementing traditional library services, areas for applying big data systems in libraries, the concept of recommender systems and how it works, adopting recommender systems in libraries for maximum benefits, tools, and techniques for setting up big data recommender systems in libraries, challenges of big data recommender systems in libraries in Nigeria and strategies for overcoming big data challenges in library systems. The paper is based on a contextual analysis of literature from various scholarly works. The paper will also proffer recommendations based on the study

INSPIRAL: investigating portals for information resources and learning. Final project report

INSPIRAL's aims were to identify and analyse, from the perspective of the UK HE learner, the nontechnical, institutional and end-user issues with regard to linking VLEs and digital libraries, and to make recommendations for JISC strategic planning and investment. INSPIRAL's objectives -To identify key stakeholders with regard to the linkage of VLEs, MLEs and digital libraries -To identify key stakeholder forum points and dissemination routes -To identify the relevant issues, according to the stakeholders and to previous research, pertaining to the interaction (both possible and potential) between VLEs/MLEs and digital libraries -To critically analyse identified issues, based on stakeholder experience and practice; output of previous and current projects; and prior and current research -To report back to JISC and to the stakeholder communities, with results situated firmly within the context of JISC's strategic aims and objectives

A machine learning approach for mapping and accelerating multiple sclerosis research

The medical field, as many others, is overwhelmed with the amount of research-related information available, such as journal papers, conference proceedings and clinical trials. The task of parsing through all this information to keep up to date with the most recent research findings on their area of expertise is especially difficult for practitioners who must also focus on their clinical duties. Recommender systems can help make decisions and provide relevant information on specific matters, such as for these clinical practitioners looking into which research to prioritize. In this paper, we describe the early work on a machine learning approach, which through an intelligent reinforcement learning approach, maps and recommends research information (papers and clinical trials) specifically for multiple sclerosis research. We tested and evaluated several different machine learning algorithms and present which one is the most promising in developing a complete and efficient model for recommending relevant multiple sclerosis research.info:eu-repo/semantics/publishedVersio