The graph-based behavior-aware recommendation for interactive news
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The relationship between employees and their employers has been conceptualized as involving a “psychological contract” (PC). The PC construct is assumed by many to have a key role to play in understanding organizational behavior, and there has been a proliferation of writing regarding PCs in recent years. The history of the construct, however, has remained under‐reported, and largely undiscussed. This articles seeks to contribute to the evolution of the PC construct by providing a richer, more thorough historical perspective than can be presently found in the PC literature. The origins and early development of the PC construct are traced through a review of books, articles, and unpublished dissertations. Observations regarding historical developments are linked to the current state of the literature, and the implication of these observations for the future direction of the PC literature is briefly discussed.
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In the recent years, the Web has undergone a tremendous growth regarding both content and users. This has lead to an information overload problem in which people are finding it increasingly difficult to locate the right information at the right time. Recommender systems have been developed to address this problem, by guiding users through the big ocean of information. Until now, recommender systems have been extensively used within e-commerce and communities where items like movies, music and articles are recommended. More recently, recommender systems have been deployed in online music players, recommending music that the users probably will like. This thesis will present the design, implementation, testing and evaluation of a recommender system within the music domain, where three different approaches for producing recommendations are utilized. Testing each approach is done by first conducting live user experiments and then measure recommender precision using offline analysis. Our results show that the functionality of the recommender system is satisfactory, and that recommender precision differs for the three filtering approaches.
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The purpose of this study is to provide a comprehensive overview of the latest developments in the field of recommender systems. In order to provide an overview of the current state of affairs in this sector and highlight the latest developments in recommender systems, the research papers available in this area were analyzed. The place of recommender systems in the modern world was defined, their relevance and role in people's daily lives in the modern information environment were highlighted. The advantages of recommender systems and their main properties are considered. In order to formally define the concept of recommender systems, a general scheme of recommender systems was provided and a formal task was formulated. A review of different types of recommender systems is carried out. It has been determined that personalized recommender systems can be divided into content filtering-based systems, collaborative filtering-based systems, and hybrid recommender systems. For each type of system, the author defines them and reviews the latest relevant research papers on a particular type of recommender system. The challenges faced by modern recommender systems are separately considered. It is determined that such challenges include the issue of robustness of recommender systems (the ability of the system to withstand various attacks), the issue of data bias (a set of various data factors that lead to a decrease in the effectiveness of the recommender system), and the issue of fairness, which is related to discrimination against users of recommender systems. Overall, this study not only provides a comprehensive explanation of recommender systems, but also provides information to a large number of researchers interested in recommender systems. This goal was achieved by analyzing a wide range of technologies and trends in the service sector, which are areas where recommender systems are used.
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A recommender system, which might assist in providing clients with new information and a better experience, is becoming increasingly popular in this era of modernization. Recommender systems are often used by various platforms to provide new products to consumers, which may also help in improving product sales. Additionally, the recommender system is essential in academic domains. It is common for users to take a while to find and access the materials they need. The recommender system is now available, which could reduce the time spent looking for materials and improve student achievement. Therefore, it is crucial to explore more on the theory and implementation of the recommender system. This paper aims to study a few types of recommender system techniques and implement it in the research article recommender system. Additionally, related research on each of the three recommender systems will be reviewed, along with a description of the related study, the dataset used, and the evaluation method.
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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.
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This chapter presents a brief and systematic overview of four major advanced recommender systems: group recommender systems, context-aware recommender systems, multi-criteria recommender systems, and cross-domain recommender systems. These advanced recommendations are characterized and compared in a unifying model as extensions of basic recommender systems. Future research topics and directions in the area of advanced personalized recommendations are discussed. Advanced recommender technologies will continue to advance.
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