An educational recommender system based on argumentation theory

You are free to use the manuscript version of your article for internal, educational or other purposes of your own institution, company or funding agency[EN] Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences. In virtual learning environments, Educational Recommender Systems deliver learning objects according to the student's characteristics, preferences and learning needs. A learning object is an educational content unit, which once found and retrieved may assist students in their learning process. In previous work, authors have designed and evaluated several recommendation techniques for delivering the most appropriate learning object for each specific student. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques. However, traditional hybridization methods fail when the learning objects delivered by each recommendation technique are very different from those selected by the other techniques (there is no agreement about the best learning object to recommend). In this paper, we present a new recommendation method based on argumentation theory that is able to combine content-based, collaborative and knowledge-based recommendation techniques, or to act as a new recommendation technique. This method provides the students with those objects for which the system is able to generate more arguments to justify their suitability. It has been implemented and tested in the Federation of Learning Objects Repositories of Colombia, getting promising results.This work was partially developed with the aid of the doctoral grant offered to Paula A. Rodriguez by 'Programa Nacional de Formacion de Investigadores - COLCIENCIAS', Colombia and partially funded by the COLCIENCIAS project 1119-569-34172 from the Universidad Nacional de Colombia. It was also supported by the by the projects TIN2015-65515-C4-1-R and TIN2014-55206-R of the Spanish government and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politecnica de Valencia.Rodríguez, P.; Heras, S.; Palanca Cámara, J.; Poveda, JM.; Duque, N.; Julian Inglada, VJ. (2017). An educational recommender system based on argumentation theory. AI Communications. 30(1):19-36. https://doi.org/10.3233/AIC-170724S1936301Briguez, C. E., Budán, M. C. D., Deagustini, C. A. D., Maguitman, A. G., Capobianco, M., & Simari, G. R. (2014). Argument-based mixed recommenders and their application to movie suggestion. Expert Systems with Applications, 41(14), 6467-6482. doi:10.1016/j.eswa.2014.03.046BRIGUEZ, C. E., CAPOBIANCO, M., & MAGUITMAN, A. G. (2013). A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATION. International Journal on Artificial Intelligence Tools, 22(04), 1350021. doi:10.1142/s0218213013500218R. Burke, Hybrid recommender systems: Survey and experiments, User Modelingand User-Adapted Interaction (2002).Chesñevar, C., Maguitman, A. G., & González, M. P. (2009). Empowering Recommendation Technologies Through Argumentation. Argumentation in Artificial Intelligence, 403-422. doi:10.1007/978-0-387-98197-0_20Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of Recommender Systems to Support Learning. Recommender Systems Handbook, 421-451. doi:10.1007/978-1-4899-7637-6_12N.D. Duque, D.A. Ovalle and J. Moreno, Objetos de aprendizaje, repositorios y federaciones... conocimiento para todos. Universidad Nacional de Colombia, 2015.Dwivedi, P., & Bharadwaj, K. K. (2013). e-Learning recommender system for a group of learners based on the unified learner profile approach. Expert Systems, 32(2), 264-276. doi:10.1111/exsy.12061GARCÍA, A. J., & SIMARI, G. R. (2004). Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming, 4(1+2), 95-138. doi:10.1017/s1471068403001674Gunawardana, A., & Shani, G. (2015). Evaluating Recommender Systems. Recommender Systems Handbook, 265-308. doi:10.1007/978-1-4899-7637-6_8Heras, S., Botti, V., & Julián, V. (2012). Argument-based agreements in agent societies. Neurocomputing, 75(1), 156-162. doi:10.1016/j.neucom.2011.02.022Heras, S., Rebollo, M., & Julián, V. (s. f.). A Dialogue Game Protocol for Recommendation in Social Networks. Hybrid Artificial Intelligence Systems, 515-522. doi:10.1007/978-3-540-87656-4_64P.A. Kirschner, S.J. Buckingham-Shum and C.S. Carr, Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making, Springer Science & Business Media, 2012.Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4), 571-604. doi:10.1007/s10462-015-9440-zLearning Technology Standards Committee, IEEE Standard for Learning Object Metadata, Institute of Electrical and Electronics Engineers, New York, 2002.Leite, W. L., Svinicki, M., & Shi, Y. (2009). Attempted Validation of the Scores of the VARK: Learning Styles Inventory With Multitrait–Multimethod Confirmatory Factor Analysis Models. Educational and Psychological Measurement, 70(2), 323-339. doi:10.1177/0013164409344507Li, H., Oren, N., & Norman, T. J. (2012). Probabilistic Argumentation Frameworks. Lecture Notes in Computer Science, 1-16. doi:10.1007/978-3-642-29184-5_1CACM Staff. (2009). Recommendation algorithms, online privacy, and more. Communications of the ACM, 52(5), 10-11. doi:10.1145/1506409.1506434Ossowski, S., Sierra, C., & Botti, V. (2012). Agreement Technologies: A Computing Perspective. Agreement Technologies, 3-16. doi:10.1007/978-94-007-5583-3_1Palanca, J., Heras, S., Jorge, J., & Julian, V. (2015). Towards persuasive social recommendation. ACM SIGAPP Applied Computing Review, 15(2), 41-49. doi:10.1145/2815169.2815173Recio-García, J. A., Quijano, L., & Díaz-Agudo, B. (2013). Including social factors in an argumentative model for Group Decision Support Systems. Decision Support Systems, 56, 48-55. doi:10.1016/j.dss.2013.05.007Rodríguez, P., Duque, N., & Ovalle, D. A. (2015). Multi-agent System for Knowledge-Based Recommendation of Learning Objects Using Metadata Clustering. Communications in Computer and Information Science, 356-364. doi:10.1007/978-3-319-19033-4_31Rodríguez, P. A., Ovalle, D. A., & Duque, N. D. (2015). A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories. Learning and Collaboration Technologies, 291-300. doi:10.1007/978-3-319-20609-7_28M. Salehi, M. Pourzaferani and S.A. Razavi, Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model, Egyptian Informatics Journal (2013).Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27-30. doi:10.5120/7218-0024Sinha, R., & Swearingen, K. (2002). The role of transparency in recommender systems. CHI ’02 extended abstracts on Human factors in computing systems - CHI ’02. doi:10.1145/506443.506619Van de Sompel, H., Chute, R., & Hochstenbach, P. (2008). The aDORe federation architecture: digital repositories at scale. International Journal on Digital Libraries, 9(2), 83-100. doi:10.1007/s00799-008-0048-7Vekariya, V., & Kulkarni, G. R. (2012). Notice of Violation of IEEE Publication Principles - Hybrid recommender systems: Survey and experiments. 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP). doi:10.1109/dictap.2012.621540

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Recommending Learning Objects with Arguments and Explanations

[EN] The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system's operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government, and by the Generalitat Valenciana (PROMETEO/2018/002) project.Heras, S.; Palanca Cámara, J.; Rodriguez, P.; Duque-Méndez, N.; Julian Inglada, VJ. (2020). Recommending Learning Objects with Arguments and Explanations. Applied Sciences. 10(10):1-18. https://doi.org/10.3390/app10103341S1181010Zapalska, A., & Brozik, D. (2006). Learning styles and online education. Campus-Wide Information Systems, 23(5), 325-335. doi:10.1108/10650740610714080Rodríguez, P., Heras, S., Palanca, J., Poveda, J. M., Duque, N., & Julián, V. (2017). An educational recommender system based on argumentation theory. AI Communications, 30(1), 19-36. doi:10.3233/aic-170724Chen, L., & Pu, P. (2011). Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1-2), 125-150. doi:10.1007/s11257-011-9108-6He, C., Parra, D., & Verbert, K. (2016). Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications, 56, 9-27. doi:10.1016/j.eswa.2016.02.013Vig, J., Sen, S., & Riedl, J. (2009). Tagsplanations. Proceedings of the 14th international conference on Intelligent user interfaces. doi:10.1145/1502650.1502661Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2009). MoviExplain. Proceedings of the third ACM conference on Recommender systems - RecSys ’09. doi:10.1145/1639714.1639777Fogg, B. J. (2002). Persuasive technology. Ubiquity, 2002(December), 2. doi:10.1145/764008.763957Benbasat, I., & Wang, W. (2005). Trust In and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72-101. doi:10.17705/1jais.00065Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27-30. doi:10.5120/7218-0024Salehi, M., Pourzaferani, M., & Razavi, S. A. (2013). Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egyptian Informatics Journal, 14(1), 67-78. doi:10.1016/j.eij.2012.12.001Dwivedi, P., & Bharadwaj, K. K. (2013). e-Learning recommender system for a group of learners based on the unified learner profile approach. Expert Systems, 32(2), 264-276. doi:10.1111/exsy.12061Tarus, J. K., Niu, Z., & Mustafa, G. (2017). Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21-48. doi:10.1007/s10462-017-9539-5BRIGUEZ, C. E., CAPOBIANCO, M., & MAGUITMAN, A. G. (2013). A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATION. International Journal on Artificial Intelligence Tools, 22(04), 1350021. doi:10.1142/s0218213013500218Recio-García, J. A., Quijano, L., & Díaz-Agudo, B. (2013). Including social factors in an argumentative model for Group Decision Support Systems. Decision Support Systems, 56, 48-55. doi:10.1016/j.dss.2013.05.007Briguez, C. E., Budán, M. C. D., Deagustini, C. A. D., Maguitman, A. G., Capobianco, M., & Simari, G. R. (2014). Argument-based mixed recommenders and their application to movie suggestion. Expert Systems with Applications, 41(14), 6467-6482. doi:10.1016/j.eswa.2014.03.046Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4), 571-604. doi:10.1007/s10462-015-9440-zThe VARK Questionnaire-Spanish Versionhttps://vark-learn.com/wp-content/uploads/2014/08/The-VARK-Questionnaire-Spanish.pdfGARCÍA, A. J., & SIMARI, G. R. (2004). Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming, 4(1+2), 95-138. doi:10.1017/s1471068403001674Gelfond, M., & Lifschitz, V. (1991). Classical negation in logic programs and disjunctive databases. New Generation Computing, 9(3-4), 365-385. doi:10.1007/bf03037169Snow, R. E. (1991). Aptitude-treatment interaction as a framework for research on individual differences in psychotherapy. Journal of Consulting and Clinical Psychology, 59(2), 205-216. doi:10.1037/0022-006x.59.2.20

Recommending Learning Videos for MOOCs and Flipped Classrooms

[EN] New teaching approaches are emerging in higher education, such as flipped classrooms. In addition, academic institutions are offering new types of training like Massive Online Open Courses. Both of these new ways of education require high-quality learning objects for their success, with learning videos being the most common to provide theoretical concepts. This paper describes a hybrid learning recommender system based on content-based techniques, which is able to recommend useful videos to learners and teachers from a learning video repository. This hybrid technique has been successfully applied to a real scenario such as the central video repository of the Universitat Politècnica de València.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and TIN2017-89156-R projects of the Spanish government, and PROMETEO/2018/002 project of Generalitat Valenciana. J. Jordán and V. Botti are funded by UPV PAID-06-18 project. J. Jordán is also funded by grant APOSTD/2018/010 of Generalitat Valenciana - Fondo Social Europeo.Jordán, J.; Valero Cubas, S.; Turró, C.; Botti Navarro, VJ. (2020). Recommending Learning Videos for MOOCs and Flipped Classrooms. Springer. 146-157. https://doi.org/10.1007/978-3-030-49778-1_12S146157Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl.-Based Syst. 22(4), 261–265 (2009)Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2012). https://doi.org/10.1007/s11280-012-0187-zvan Dijck, J., Poell, T.: Higher education in a networked world: European responses to U.S. MOOCs. Int. J. Commun.: IJoC 9, 2674–2692 (2015)Dwivedi, P., Bharadwaj, K.K.: e-learning recommender system for a group of learners based on the unified learner profile approach. Expert Syst. 32(2), 264–276 (2015)Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)Institute and Committee of Electrical and Electronics Engineers: Learning Technology Standards: IEEE Standard for Learning Object Metadata. IEEE Standard 1484.12.1 (2002)Klašnja-Milićević, A., Ivanović, M., Nanopoulos, A.: Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 44(4), 571–604 (2015). https://doi.org/10.1007/s10462-015-9440-zMaassen, P., Nerland, M., Yates, L. (eds.): Reconfiguring Knowledge in Higher Education. Higher Education Dynamics, vol. 50. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-72832-2MLLP research group, Universitat Politècnica de València: Tlp: The translectures-upv platform. http://www.mllp.upv.es/tlpO’Flaherty, J., Phillips, C.: The use of flipped classrooms in higher education: a scoping review. Internet High. Educ. 25, 85–95 (2015)Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th international conference on World Wide Web, pp. 521–530 (2007)Rodríguez, P., Heras, S., Palanca, J., Duque, N., Julián, V.: Argumentation-based hybrid recommender system for recommending learning objects. In: Rovatsos, M., Vouros, G., Julian, V. (eds.) EUMAS/AT -2015. LNCS (LNAI), vol. 9571, pp. 234–248. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33509-4_19Roehl, A., Reddy, S.L., Shannon, G.J.: The flipped classroom: an opportunity to engage millennial students through active learning strategies. J. Fam. Consum. Sci. 105, 44–49 (2013)Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)Stoica, A.S., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C.: A semi-supervised method to classify educational videos. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 218–228. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_19Tarus, J.K., Niu, Z., Yousif, A.: A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Gener. Comput. Syst. 72, 37–48 (2017)Tucker, B.: The flipped classroom. Online instruction at home frees class time for learning. Educ. Next Winter 2012, 82–83 (2012)Turcu, G., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C.: Towards a custom designed mechanism for indexing and retrieving video transcripts. 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Using a Hybrid Recommending System for Learning Videos in Flipped Classrooms and MOOCs

[EN] New challenges in education require new ways of education. Higher education has adapted to these new challenges by means of offering new types of training like massive online open courses and by updating their teaching methodology using novel approaches as flipped classrooms. These types of training have enabled universities to better adapt to the challenges posed by the pandemic. In addition, high quality learning objects are necessary for these new forms of education to be successful, with learning videos being the most common learning objects to provide theoretical concepts. This paper describes a new approach of a previously presented hybrid learning recommender system based on content-based techniques, which was capable of recommend useful videos to learners and lecturers from a learning video repository. In this new approach, the content-based techniques are also combined with a collaborative filtering module, which increases the probability of recommending relevant videos. This hybrid technique has been successfully applied to a real scenario in the central video repository of the Universitat Politècnica de València.This research was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and TIN2017-89156-R projects of the Spanish government, and PROMETEO/2018/002 project of Generalitat Valenciana.Jordán, J.; Valero Cubas, S.; Turró, C.; Botti, V. (2021). Using a Hybrid Recommending System for Learning Videos in Flipped Classrooms and MOOCs. Electronics. 10(11):1-19. https://doi.org/10.3390/electronics10111226S119101

Argument-based generation and explanation of recommendations

In the recommender systems literature, it has been shown that, in addition to improving system effectiveness, explaining recommendations may increase user satisfaction, trust, persuasion and loyalty. In general, explanations focus on the filtering algorithms or the users and items involved in the generation of recommendations. However, on certain domains that are rich on user-generated textual content, it would be valuable to provide justifications of recommendations according to arguments that are explicit, underlying or related with the data used by the systems, e.g., the reasons for customers' opinions in reviews of e-commerce sites, and the requests and claims in citizens' proposals and debates of e-participation platforms. In this context, there is a need and challenging task to automatically extract and exploit the arguments given for and against evaluated items. We thus advocate to focus not only on user preferences and item features, but also on associated arguments. In other words, we propose to not only consider what is said about items, but also why it is said. Hence, arguments would not only be part of the recommendation explanations, but could also be used by the recommendation algorithms themselves. To this end, in this thesis, we propose to use argument mining techniques and tools that allow retrieving and relating argumentative information from textual content, and investigate recommendation methods that exploit that information before, during and after their filtering processesThe author thanks his supervisor Iván Cantador for his valuable support and guidance in defining this thesis project. The work is supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00

THE USE OF RECOMMENDER SYSTEMS IN WEB APPLICATIONS – THE TROI CASE

Avoiding digital marketing, surveys, reviews and online users behavior approaches on digital age are the key elements for a powerful businesses to fail, there are some systems that should preceded some artificial intelligence techniques. In this direction, the use of data mining for recommending relevant items as a new state of the art technique is increasing user satisfaction as well as the business revenues. And other related information gathering approaches in order to our systems thing and acts like humans. To do so there is a Recommender System that will be elaborated in this thesis. How people interact, how to calculate accurately and identify what people like or dislike based on their online previous behaviors. The thesis includes also the methodologies recommender system uses, how math equations helps Recommender Systems to calculate user’s behavior and similarities. The filters are important on Recommender System, explaining if similar users like the same product or item, which is the probability of neighbor user to like also. Here comes collaborative filters, neighborhood filters, hybrid recommender system with the use of various algorithms the Recommender Systems has the ability to predict whether a particular user would prefer an item or not, based on the user’s profile and their activities. The use of Recommender Systems are beneficial to both service providers and users. Thesis cover also the strength and weaknesses of Recommender Systems and how involving Ontology can improve it. Ontology-based methods can be used to reduce problems that content-based recommender systems are known to suffer from. Based on Kosovar’s GDP and youngsters job perspectives are desirable for improvements, the demand is greater than the offer. I thought of building an intelligence system that will be making easier for Kosovars to find the appropriate job that suits their profile, skills, knowledge, character and locations. And that system is called TROI Search engine that indexes and merge all local operating job seeking websites in one platform with intelligence features. Thesis will present the design, implementation, testing and evaluation of a TROI search engine. Testing is done by getting user experiments while using running environment of TROI search engine. Results show that the functionality of the recommender system is satisfactory and helpful

Towards persuasive social recommendation: knowledge model

[EN] The exponential growth of social networks makes fingerprint let by users on the Internet a great source of information, with data about their preferences, needs, goals, profile and social environment. These data are distributed across di↵erent sources of information (social networks, blogs, databases, etc.) that may contain inconsistencies and their accuracy is uncertain. Paradoxically, this unprecedented availability of heterogeneous data has meant that users have more information available than they actually are able to process and understand to extract useful knowledge from it. Therefore, new tools that help users in their decision-making processes within the network (e.g. which friends to contact with or which products to consume) are needed. In this paper, we show how we have used a graph-based model to extract and model data and transform it in valuable knowledge to develop a persuasive social recommendation system1.This work was partially supported by the project MINE-CO/FEDER TIN2012-365686-C03-01 of the Spanish government and by the Spanish Ministry of Education, Culture and Sports under the Program for R&D Valorisation and Joint Resources VLC/CAMPUS, as part of the Campus of International Excellence Program (Ref. SP20140788).Palanca Cámara, J.; Heras Barberá, SM.; Jorge Cano, J.; Julian Inglada, VJ. (2015). Towards persuasive social recommendation: knowledge model. ACM SIGAPP Applied Computing Review. 15(2):41-49. https://doi.org/10.1145/2815169.2815173S4149152Desel, J., Pernici, B., Weske, M. 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Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning. Journal of Digital Information, 10(2), 4-24.The following article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning to support lifelong learners. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system from one context and transfer it to another context or domain. The article describes a number of distinct differences for personalized recommendation to consumers in contrast to recommendations to learners. Similarities and differences are translated into specific demands for learning and specific requirements for personal recommendation systems. It further suggests an evaluation approach for recommender systems in technology-enhanced learning.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Revisiting the challenges and surveys in text similarity matching and detection methods

The massive amount of information from the internet has revolutionized the field of natural language processing. One of the challenges was estimating the similarity between texts. This has been an open research problem although various studies have proposed new methods over the years. This paper surveyed and traced the primary studies in the field of text similarity. The aim was to give a broad overview of existing issues, applications, and methods of text similarity research. This paper identified four issues and several applications of text similarity matching. It classified current studies based on intrinsic, extrinsic, and hybrid approaches. Then, we identified the methods and classified them into lexical-similarity, syntactic-similarity, semantic-similarity, structural-similarity, and hybrid. Furthermore, this study also analyzed and discussed method improvement, current limitations, and open challenges on this topic for future research directions