An Ontology-Based Recommendation System Using Long-Term and Short-Term Preferences
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Personalized information retrieval and recommendation systems have been proposed to deliver the right information to users with different interests. However, most of previous systems are using keyword frequencies as the main factor for personalization, and as a result, they could not analyze semantic relations between words. Also, previous methods often fail to provide the documents that are related semantically with the query words. To solve these problems, we propose a recommendation system which provides relevant documents to users by identifying semantic relations between an ontology that semantically represents the documents crawled by a Web robot and user behavior history. Recommendation is mainly based on content-based similarity, semantic similarity, and preference weights.Keywords:
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A huge number of information is available nowadays and it keeps increasing every day. As such, the need for recommender system to recommend relevant items or information is in high demand. Furthermore, recommender system is expected to deliver relevant items from a trustable source. In this paper, we proposed a new trust calculation that is incorporated into a hybrid recommender system. Our new trust calculation is calculated based on user's score in a particular system, and its potential implementation is demonstrated through a prototype design.
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It has become challenging to find relevant information needed for the user. In this evolving world of technology finding relevant data has become very crucial as most of the businesses revolve around data. To solve this problem, recommender systems are used. It has become very difficult to get relevant data without any proper recommender systems. Today, in every field recommender systems are used to provide relevant data to user on the basis of their choices, needs or interests. Content based recommender system and collaborative filtering recommender systems are two basics types of recommender systems are available. These two systems can be combined to make recommender system more efficient, these combined systems are called hybrid systems.
The purpose of the paper is to help new researchers to understand the working of basic recommender systems and identifies new research area for further improvement of recommender system.
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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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Researchers still believe that the information filtering system/ collaborating system is a recommender system or a recommendation system. It is used to predict the or 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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In this paper, we present an ontology-based and student' model to recommend the most suited material of study for each student in e-learning environments. Our aim is to increment the current systems personalization capabilities for student's in different scenarios making use of learning objects ontology. This approach is being developed in an e-learning environment: the AdaptWeb system. The main features of the recommendations aspects for student's profile are described and we use some examples to discuss and illustrate how to provide this personalization.
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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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