FUZZY RELATIONS BASED INTELLIGENT INFORMATION RETRIEVAL FOR DIGITAL LIBRARY USERS

It is known that today information and library systems are one of the main sources of information needs of the population and have many users. Information and library systems have a large amount of valuable information resources and information retrieval services have been established to allow users to find the necessary literature. We know that search engines take requests and return results they think are relevant. As a result, the user again faces the problem of finding what he needs among the many sources of information provided.Today, a number of information systems effectively use recommendation systems based on artificial intelligence to recommend objects. In information and library systems, high efficiency can be achieved by identifying the information needs of users and recommending relevant literature.To do this, it is necessary to determine the information needs of library users by analyzing information about their age, interests, level of knowledge in a particular area, previous requests, professions, etc. By introducing recommender systems into library information systems, it is possible to facilitate the work of librarians, increase speed and accuracy finding the necessary source of information, increase the efficiency of management and the level of satisfaction of the information needs of the population.The article proposes a fuzzy model for solving the problem of assessing the needs of users of information and library systems and recommending relevant literature to them.

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl