Research opportunities for argumentation in social networks

Nowadays, many websites allow social networking between their users in an explicit or implicit way. In this work, we show how argumentation schemes theory can provide a valuable help to formalize and structure on-line discussions and user opinions in decision support and business oriented websites that held social networks between their users. Two real case studies are studied and analysed. Then, guidelines to enhance social decision support and recommendations with argumentation are provided.This work summarises results of the authors joint research, funded by an STMS of the Agreement Technologies COST Action 0801, by the Spanish government grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO 2008/051].Heras Barberá, SM.; Atkinson, KM.; Botti Navarro, VJ.; Grasso, F.; Julian Inglada, VJ.; Mcburney, PJ. (2013). Research opportunities for argumentation in social networks. Artificial Intelligence Review. 39(1):39-62. doi:10.1007/s10462-012-9389-0S3962391Amgoud L (2009) Argumentation for decision making. Argumentation in artificial intelligence. Springer, BerlinAnderson P (2007) What is Web 2.0? Ideas, technologies and implications for education. JISC Iechnology and Standards Watch reportBentahar J, Meyer CJJ, Moulin B (2007) Securing agent-oriented systems: an argumentation and reputation-based approach. In: Proceedings of the 4th international conference on information technology: new generations (ITNG 2007), IEEE Computer Society, pp 507–515Buckingham Shum S (2008) Cohere: towards Web 2.0 argumentation. In: Proceedings of the 2nd international conference on computational models of argument, COMMA, pp 28–30Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12:331–370Cartwright D, Atkinson K (2008) Political engagement through tools for argumentation. In: Proceedings of the second international conference on computational models of argument (COMMA 2008), pp 116–127Chesñevar C, McGinnis J, Modgil S, Rahwan I, Reed C, Simari G, South M, Vreeswijk G, Willmott S (2006) Towards an argument interchange format. Knowl Eng Rev 21(4):293–316Chesñevar CI, Maguitman AG, Gonzàlez MP (2009) Empowering recommendation technologies through argumentation. Argumentation in artificial intelligence. Springer, Berlin, pp 403–422García AJ, Dix J, Simari GR (2009) Argument-based logic programming. Argumentation in artificial intelligence. Springer, BerlinGolbeck J (2006) Generating predictive movie recommendations from trust in social networks. In: Proceedings of the fourth international conference on trust management, LNCS, vol 3986, 93–104Gordon T, Prakken H, Walton D (2007) The Carneades model of argument and burden of proof. Artif Intell 171(10–15):875–896Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagating trust and distrust. In: Proceedings of the 13th international conference on, World Wide Web, pp 403–412Heras S, Navarro M, Botti V, Julián V (2009) Applying dialogue games to manage recommendation in social networks. In: Proceedings of the 6th international workshop on argumentation in multi-agent aystems, ArgMASHeras S, Atkinson K, Botti V, Grasso F, Julián V, McBurney P (2010a) How argumentation can enhance dialogues in social networks. In: Proceedings of the 3rd international conference on computational models of argument, COMMA, vol 216, pp 267–274Heras S, Atkinson K, Botti V, Grasso F, Julián V, McBurney P (2010b) Applying argumentation to enhance dialogues in social networks. In: ECAI 2010 workshop on computational models of natural argument, CMNA, pp 10–17Karacapilidis N, Tzagarakis M (2007) Web-based collaboration and decision making support: a multi-disciplinary approach. Web-Based Learn Teach Technol 2(4):12–23Kim D, Benbasat I (2003) Trust-related arguments in internet stores: a framework for evaluation. J Electron Commer Res 4(2):49–64Kim D, Benbasat I (2006) The effects of trust-assuring arguments on consumer trust in internet stores: application of Toulmin’s model of argumentation. Inf Syst Rese 17(3):286–300Laera L, Tamma V, Euzenat J, Bench-Capon T, Payne T (2006) Reaching agreement over ontology alignments. In: Proceedings of the 5th international semantic web conference (ISWC 2006)Lange C, Bojãrs U, Groza T, Breslin J, Handschuh S (2008) Expressing argumentative discussions in social media sites. In: Social data on the web (SDoW2008) workshop at the 7th international semantic web conferenceLinden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80Linden G, Hong J, Stonebraker M, Guzdial M (2009) Recommendation algorithms, online privacy and more. Commun ACM, 52(5)Mika P (2007) Ontologies are us: a unified model of social networks and semantics. J Web Semant 5(1):5–15Montaner M, López B, de la Rosa JL (2002) Opinion-based filtering through trust. In: Cooperative information agents VI, LNCS, vol 2446, pp 127–144Ontañón S, Plaza E (2008) Argumentation-based information exchange in prediction markets. In: Proceedings of the 5th international workshop on argumentation in multi-agent systems, ArgMASPazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web, LNCS, vol 4321, pp 325–341Rahwan I, Zablith F, Reed C (2007) Laying the foundations for a world wide argument web. Artif Intell 171(10–15):897–921Rahwan I, Banihashemi B (2008) Arguments in OWL: a progress report. In: Proceedings of the 2nd international conference on computational models of argument (COMMA), pp 297–310Reed C, Walton D (2007) Argumentation schemes in dialogue. In: Dissensus and the search for common ground, OSSA-07, volume CD-ROM, pp 1–11Sabater J, Sierra C (2002) Reputation and social network analysis in multi-agent systems. In: Proceedings of the 1st international joint conference on autonomous agents and multiagent systems, vol 1, pp 475–482Schafer JB, Konstan JA, Riedl J (2001) E-commerce recommendation applications. Data Min Knowl Discov 5:115–153Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: The adaptive web, LNCS, vol 4321, pp 291–324Schneider J, Groza T, Passant A (2012) A review of argumentation for the aocial semantic web. Semantic web-interoperability, usability, applicability. IOS Press, Washington, DCTempich C, Pinto HS, Sure Y, Staab S (2005) An argumentation ontology for distributed, loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT). In: Proceedings of the 2nd European semantic web conference, ESWC, pp 241–256Toulmin SE (1958) The uses of argument. Cambridge University Press, Cambridge, UKTrojahn C, Quaresma P, Vieira R, Isaac A (2009) Comparing argumentation frameworks for composite ontology matching. in: Proceedings of the 6th international workshop on argumentation in multi-agent systems, ArgMASTruthMapping. http://truthmapping.com/Walter FE, Battiston S, Schweitzer F (2007) A model of a trust-based recommendation system on a social network. J Auton Agents Multi-Agent Syst 16(1):57–74Walton D, Krabbe E (1995) Commitment in dialogue: basic concepts of interpersonal reasoning. State University of New York Press, New York, NYWalton D, Reed C, Macagno F (2008) Argumentation schemes. Cambridge University Press, CambridgeWells S, Gourlay C, Reed C (2009) Argument blogging. Computational models of natural argument, CMNAWyner A, Schneider J (2012) Arguing from a point of view. In: Proceedings of the first international conference on agreement technologie

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

Towards an argument-based music recommender system

The significance of recommender systems has steadily grown in recent years as they help users to access relevant items from the vast universe of possibilities available these days. However, most of the research in recommenders is based purely on quantitative aspects, i.e., measures of similarity between items or users. In this paper we introduce a novel hybrid approach to refine recommendations achieved by quantitative methods with a qualitative approach based on argumentation, where suggestions are given after considering several arguments in favor or against the recommendations. In order to accomplish this, we use Defeasible Logic Programming (DeLP) as the underlying formalism for obtaining recommendations. This approach has a number of advantages over other existing recommendation techniques.In particular, recommendations can be refined at any time by adding new polished rules, and explanations may be provided supporting each  recommendation in a way that can be easily understood by the user, by means of the computed arguments.Fil: Briguez, Cristian Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaFil: Budan, Maximiliano Celmo David. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Deagustini, Cristhian Ariel David. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaFil: Capobianco, Marcela. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Simari, Guillermo Ricardo. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentin

South American Expert Roundtable : increasing adaptive governance capacity for coping with unintended side effects of digital transformation

This paper presents the main messages of a South American expert roundtable (ERT) on the unintended side effects (unseens) of digital transformation. The input of the ERT comprised 39 propositions from 20 experts representing 11 different perspectives. The two-day ERT discussed the main drivers and challenges as well as vulnerabilities or unseens and provided suggestions for: (i) the mechanisms underlying major unseens; (ii) understanding possible ways in which rebound effects of digital transformation may become the subject of overarching research in three main categories of impact: development factors, society, and individuals; and (iii) a set of potential action domains for transdisciplinary follow-up processes, including a case study in Brazil. A content analysis of the propositions and related mechanisms provided insights in the genesis of unseens by identifying 15 interrelated causal mechanisms related to critical issues/concerns. Additionally, a cluster analysis (CLA) was applied to structure the challenges and critical developments in South America. The discussion elaborated the genesis, dynamics, and impacts of (groups of) unseens such as the digital divide (that affects most countries that are not included in the development of digital business, management, production, etc. tools) or the challenge of restructuring small- and medium-sized enterprises (whose service is digitally substituted by digital devices). We identify specific issues and effects (for most South American countries) such as lack of governmental structure, challenging geographical structures (e.g., inclusion in high-performance transmission power), or the digital readiness of (wide parts) of society. One scientific contribution of the paper is related to the presented methodology that provides insights into the phenomena, the causal chains underlying “wanted/positive” and “unwanted/negative” effects, and the processes and mechanisms of societal changes caused by digitalization

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

Persuasion and Recommendation System Applied to a Cognitive Assistant

In this paper, we present a persuasive recommendation module included in the iGenda framework. iGenda is a cognitive assistant that helps care-receivers and caregivers in the management of their activities of daily living, by resolving scheduling conflicts and promoting active aging activities. The proposed new module will allow the system to select and recommend to the users an event that potentially best suits to his/her interests (likes or medical condition). The multi-agent approach followed by the iGenda framework facilitates an easy integration of these new features. The social objective is to promote social activities and engaging the users in physical or psychological activities that improve their medical condition

Relational justice: mediation and ODR through the World Wide Web

ODR means "Online Dispute Resolution". Dialogue, negotiation and mediation are coming back as sources of contemporary law. We introduce in this paper two concepts and two related projects. We define the concepts of "relational law" and "relational justice". And, at the same time, we describe how to put them in place from a social and technological point of view. Therefore, we introduce two concrete applications: (i) the Catalan White Book on Mediation, a large project to assemble the required social and legal knowledge to draft a general statute on mediation (Catalan Government); (ii) the Ontomedia Project, a semantically-driven platform allowing end-users to negotiate and mediate their conflicts in several domains (family, commerce, environment, health care, administration…). The paper describes the state of the art of ODR services, and proposes some strategies for legal electronic institutions. A middle-out theoretical approach and a mediation core-ontology are briefly described. We situate these two projects within the next generation of Semantic Web services, and the so-called Web 2.0 and Web 3.0 developments

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