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Journal ArticleDOI

A Review of Content-Based and Context-Based Recommendation Systems

12 Feb 2021-International Journal of Emerging Technologies in Learning (ijet) (International Journal of Emerging Technology in Learning)-Vol. 16, Iss: 03, pp 274-306
TL;DR: This study has concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, the system can also recommend items according to the user’s interests.
Abstract: In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

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Citations
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Journal ArticleDOI
01 Jan 2022
TL;DR: In this article , the authors examined the development and evaluation of ontology-based recommender systems and discussed technical ontology use and the recommendation process and found that the most popular recommendation item is the learning object.
Abstract: Ontology and knowledge-based systems typically provide e-learning recommender systems. However, ontology use in such systems is not well studied in systematic detail. Therefore, this research examines the development and evaluation of ontology-based recommender systems. The study also discusses technical ontology use and the recommendation process. We identified multidisciplinary ontology-based recommender systems in 28 journal articles. These systems combined ontology with artificial intelligence, computing technology, education, education psychology, and social sciences. Student models and learning objects remain the primary ontology use, followed by feedback, assessments, and context data. Currently, the most popular recommendation item is the learning object, but learning path, feedback, and learning device could be the future considerations. This recommendation process is reciprocal and can be initiated either by the system or students. Standard ontology languages are commonly used, but standards for student profiles and learning object metadata are rarely adopted. Moreover, ontology-based recommender systems seldom use the methodology of building ontologies and hardly use other ontology methodologies. Similarly, none of the primary studies described ontology evaluation methodologies, but the systems are evaluated by nonreal students, algorithmic performance tests, statistics, questionnaires, and qualitative observations. In conclusion, the findings support the implementation of ontology methodologies and the integration of ontology-based recommendations into existing learning technologies. The study also promotes the use of recommender systems in social science and humanities courses, non-higher education, and open learning environments.

50 citations

Journal ArticleDOI
TL;DR: The results show that erythrocyte sedimentation rate, asbestos exposure and its duration time, and pleural and serum lactic dehydrogenase ratio are major risk factors of MM.
Abstract: In today’s world, lung cancer is a significant health burden, and it is one of the most leading causes of death. A leading type of lung cancer is malignant mesothelioma (MM). Most of the MM patients do not show any symptoms. Etiology plays a vital factor in the diagnosis of any disease. Positron emission tomography (PET), magnetic resonance imaging (MRI), biopsies, X-rays and blood tests are essential but costly and invasive MM risk factor identification methods. In this work, we mainly focused on the exploration of the MM risk factors. The identification of mesothelioma symptoms was carried out by utilizing the data of mesothelioma patients. However, the dataset was comprised of both healthy and mesothelioma patients. The dataset is prone to a class imbalance problem in which the number of MM patients significantly less than healthy individuals. To overcome the class imbalance problem, the synthetic minority oversampling technique has been utilized. The association rule mining-based Apriori algorithm has been applied to a preprocessed dataset. Before using the Apriori algorithm, both duplicate and irrelevant attributes were removed. Moreover, the numerical attributes were also classified into nominal attributes and the association rules were generated in the dataset. Our results show that erythrocyte sedimentation rate, asbestos exposure and its duration time, and pleural and serum lactic dehydrogenase ratio are major risk factors of MM. The severe stages of MM can be avoided by earlier identification of risk factors of the disease. The failure of identification of risk factors can lead to increased risk of multiple medical conditions, including cardiovascular diseases, mental distress, diabetes and anemia.

20 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a time sensitive heterogeneous graph neural network for news recommendation, which consists of two subnetworks: one subnet utilizes convolutional neural network and improved LSTM to learn a user's stay period on the page and click sequence characteristics as the temporal dimension feature.

11 citations

Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors found that the degree of multi-context use is a key boundary for the effectiveness of a location-based recommendation method, which may trigger users perceived privacy threat, which will reduce their satisfaction and participation intention.
Abstract: Reading information in news feed apps has become a kind of popular content consumption in recent years. However, there are contradictory conclusions about the recommendation strategies. Although some previous research has studied the algorithms to improve the accuracy of the combined of relevancy and diversity strategies, how to compromise them according to user attitude and behavior from individual level is limited. This study aims to solve the dilemma at a theoretical level, and the authors find that the degree of multi-context use is a key boundary. Specifically, 1) the location-based recommendation method may lose effectiveness when the degree of a multi-context is high. 2) The users who prefer to use news feed systems in various contexts in their daily life are more likely to read diverse information. The authors suppose that with the increase of the degree of multi-context use, the location-based recommendation may trigger users perceived privacy threat, which will reduce their satisfaction and participation intention.

11 citations

Journal ArticleDOI
TL;DR: An academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components and provides instructor with tools for building knowledge component network and setting question of course.
Abstract: Study in Literatures shows that tracing knowledge state of student is corner stone of intelligent tutoring system for personalized learning. In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components. This question recommender is discussed with theoretical analysis, and designed and implemented in software engineering way. It provides instructor with tools for building knowledge component network and setting question of course. It also makes student personalize practice questions of course. This question recommender is planned to deploy in real learning context for the future validation of how well such question recommendation improves performance and saves practice time for student.

6 citations

References
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Journal ArticleDOI
TL;DR: Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy are shown.
Abstract: We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

785 citations

Proceedings ArticleDOI
12 Jan 2003
TL;DR: The results of a nine month field study show that although there are several challenges to overcome, mobile recommender systems have the potential to provide value to their users today.
Abstract: Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our experience with implementing a recommender system on a PDA that is occasionally connected to the network. This interface helps users of the MovieLens movie recommendation service select movies to rent, buy, or see while away from their computer. The results of a nine month field study show that although there are several challenges to overcome, mobile recommender systems have the potential to provide value to their users today

581 citations


"A Review of Content-Based and Conte..." refers methods in this paper

  • ...[12] Y. Naudet, L. Schwartz, S. Mignon, and M. Foulonneau, "Applications of user and context-aware recommendations using ontologies," in Proceedings of the 22nd Conference on l'Interaction Homme-Machine, 2010, pp. 165-172. https://doi.org/10.1145/1941007.194 1038 [13] B. N. Miller, I. Albert, S. K. Lam, J. A. Konstan, and J. Riedl, "Movielens unplugged: experiences with an occasionally connected recommender system," in Proceedings of the 8th international conference on Intelligent user interfaces, 2003, pp. 263-266. https://doi. org/10.1145/604045.604094 [14] L. Ardissono, C. Gena, P. Torasso, F. Bellifemine, A. Difino, and B. Negro, "User modeling and recommendation techniques for personalized electronic program guides," in Personalized Digital Television: Springer, 2004, pp. 3-26. https://doi.org/10.1007/1-40202164-x_1 [15] Z. Yu, X. Zhou, D. Zhang, C.-Y. Chin, X. Wang, and J. Men, "Supporting context-aware media recommendations for smart phones," IEEE Pervasive Computing, vol. 5, no. 3, pp. 68-75, 2006. https://doi.org/10.1109/mprv.2006.61 302 http://www.i-jet.org [16] Z. Yu, Y. Nakamura, S. Jang, S. Kajita, and K. Mase, "Ontology-based semantic recommendation for context-aware e-learning," in International Conference on Ubiquitous Intelligence and Computing, 2007: Springer, pp. 898-907. https://doi.org/10.1007/978-3540-73549-6_88 [17] T. Hussein, T. Linder, W. Gaulke, J. Ziegler, and L. Bergmann, "Context-aware recommendations on rails," in Workshop on Context-Aware Recommender Systems (CARS-2009) in conjunction with the 3rd ACM Conference on Recommender Systems (ACM RecSys 2009), New York, NY, USA, 2009. https://doi.org/10.1145/1639714.1639 806 [18] C. Rack, S. Arbanowski, and S. Steglich, "Context-aware, ontology-based recommendations," in International Symposium on Applications and the Internet Workshops (SAINTW'06), 2006: IEEE, pp. 7 pp.-104. https://doi.org/10.1109/saintw.2006.13 [19] A. Costa, R. Guizzardi, G. Guizzardi, and J. Pereira Filho, "COReS: Context-aware, Ontology-based Recommender system for Service recommendation," in Proc....

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  • ...Some recommender frameworks, for example, Movielens [13], depend on collaborative filtering to customize the proposal of things....

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Proceedings ArticleDOI
05 Sep 2012
TL;DR: This paper implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users.
Abstract: The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets These datasets are connected with each other to form the so called Linked Open Data cloud As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics

278 citations


"A Review of Content-Based and Conte..." refers background in this paper

  • ...com order scientific categorizations for books, DVDs, CDs, and clothing [40]....

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Proceedings ArticleDOI
22 Oct 2001
TL;DR: In this article, the authors explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences.
Abstract: Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.

238 citations

Journal ArticleDOI
TL;DR: A hybrid recommendation approach to synergize content-based, Bayesian-classifier, and rule-based methods for media recommendation, adaptation, and delivery for smart phones is proposed.
Abstract: A context-aware media recommendation platform uses an NtimesM-dimensional model and a hybrid processing approach to support media recommendation, adaptation, and delivery for smart phones. To provide media recommendations for smart phones based on all three context categories, we present a generic and flexible NtimesM-dimensional (N2M) recommendation model. The model considers context information ranging from user preference and situation to device and network capability as input for both content and presentation recommendations. We propose a hybrid recommendation approach to synergize content-based, Bayesian-classifier, and rule-based methods. Based on the recommendation model and hybrid-processing approach, we built a context-aware media recommendation platform called CoMeR to support media recommendation, adaptation, and delivery for smart phones

230 citations


"A Review of Content-Based and Conte..." refers background or methods in this paper

  • ...For instance, [15] designed an architecture named: The CoMeR architecture to support media recommendation, adaptation, and delivery for smartphones....

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  • ..., [15] developed a context representation model of ontology-based recommender using OWL....

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  • ...In contrast, Yuichi Nakamura [16], Innar Liiv [20], Tanel Tammet [20], Zhiwen Yu [16], and Xingshe Zhou [15] recommended learning materials, paths, and goals....

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