Hybrid Recommender Systems: A Systematic Literature Review

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc

Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

[EN] This work proposes a persuasion model based on argumentation theory and users' characteristics for improving the use of resources in bike sharing systems, fostering the use of the bicycles and thus contributing to greater energy sustainability by reducing the use of carbon-based fuels. More specifically, it aims to achieve a balanced network of pick-up and drop-off stations in urban areas with the help of the users, thus reducing the dedicated management trucks that redistribute bikes among stations. The proposal aims to persuade users to choose different routes from the shortest route between a start and an end location. This persuasion is carried out when it is not possible to park the bike in the desired station due to the lack of parking slots, or when the user is highly influenceable. Differently to other works, instead of employing a single criteria to recommend alternative stations, the proposed system can incorporate a variety of criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules. The proposed framework is showcased with an example scenario of a bike sharing system.This work was supported by the projects TIN2015-65515-C4-1-R and TIN2017-89156-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 Politècnica de València.Diez-Alba, C.; Palanca Cámara, J.; Sanchez-Anguix, V.; Heras, S.; Giret Boggino, AS.; Julian Inglada, VJ. (2019). Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach. Energies. 12(4):1-19. https://doi.org/10.3390/en12040662S119124Erdoğan, G., Laporte, G., & Wolfler Calvo, R. (2014). The static bicycle relocation problem with demand intervals. European Journal of Operational Research, 238(2), 451-457. doi:10.1016/j.ejor.2014.04.013Alvarez-Valdes, R., Belenguer, J. M., Benavent, E., Bermudez, J. D., Muñoz, F., Vercher, E., & Verdejo, F. (2016). Optimizing the level of service quality of a bike-sharing system. Omega, 62, 163-175. doi:10.1016/j.omega.2015.09.007Schuijbroek, J., Hampshire, R. C., & van Hoeve, W.-J. (2017). Inventory rebalancing and vehicle routing in bike sharing systems. European Journal of Operational Research, 257(3), 992-1004. doi:10.1016/j.ejor.2016.08.029Li, L., & Shan, M. (2016). Bidirectional Incentive Model for Bicycle Redistribution of a Bicycle Sharing System during Rush Hour. Sustainability, 8(12), 1299. doi:10.3390/su8121299Anagnostopoulou, E., Bothos, E., Magoutas, B., Schrammel, J., & Mentzas, G. (2018). Persuasive Technologies for Sustainable Mobility: State of the Art and Emerging Trends. Sustainability, 10(7), 2128. doi:10.3390/su10072128Galbrun, E., Pelechrinis, K., & Terzi, E. (2016). Urban navigation beyond shortest route: The case of safe paths. Information Systems, 57, 160-171. doi:10.1016/j.is.2015.10.005Ferrara, J. (2013). Games for Persuasion: Argumentation, Procedurality, and the Lie of Gamification. Games and Culture, 8(4), 289-304. doi:10.1177/1555412013496891Fei, X., Shah, N., Verba, N., Chao, K.-M., Sanchez-Anguix, V., Lewandowski, J., … Usman, Z. (2019). CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey. Future Generation Computer Systems, 90, 435-450. doi:10.1016/j.future.2018.06.042Faed, A., Hussain, O. K., & Chang, E. (2013). A methodology to map customer complaints and measure customer satisfaction and loyalty. Service Oriented Computing and Applications, 8(1), 33-53. doi:10.1007/s11761-013-0142-6Xu, W., Li, Z., Cheng, C., & Zheng, T. (2012). Data mining for unemployment rate prediction using search engine query data. Service Oriented Computing and Applications, 7(1), 33-42. doi:10.1007/s11761-012-0122-2GARCÍA, 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/s147106840300167

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

Promoting Diversity in Academic Research Communities Through Multivariate Expert Recommendation

Expert recommendation is the process of identifying individuals who have the appropriate knowledge and skills to achieve a specific task. It has been widely used in the educational environment mainly in the hiring process, paper-reviewer assignment, and assembling conference program committees. In this research, we highlight the problem of diversity and fair representation of underrepresented groups in expertise recommendation, factors that current expertise recommendation systems rarely consider. We introduce a novel way to model experts in academia by considering demographic attributes in addition to skills. We use the h-index score to quantify skills for a researcher and we identify five demographic features with which to represent a researcher\u27s demographic profile. We highlight the importance of these features and their role in bias within the academic environment. We utilize these demographic features within an expert recommender system in academia to achieve demographic diversity and increase the exposure of the underrepresented groups using two approaches. In the first approach, we present three different algorithms for scholar recommendation: expertise-based, diversity-based, and a hybrid algorithm that uses a tuning parameter to calibrate the balance between expertise loss and diversity gain. To evaluate the ranking produced by these algorithms, we introduce a modified normalized Discounted Cumulative Gain (nDCG) version that supports multi-dimensional features, and we report diversity gain from each method. Our results show that we can achieve the best possible balance between diversity gain and expertise loss when the tuning parameter value is set around 0.4, giving nearly equal weight to both expertise and diversity. Finally, we explore diversity from the lens of the demographic parity and develop two algorithms to achieve a representative group that reflects the demographics of the recommendation pool. One is inspired by Hill Climbing, a mathematical optimization technique, wherein a solution is built gradually to the problem, and the other one is inspired by the problem of seat allocation in electoral voting systems. We evaluated these algorithms by comparing them to the hybrid algorithm from the previous approach. Our evaluation shows that both approaches provide a better diversity gain as compared to the hybrid algorithm. However, Hill Climbing Diversity is more effective when it comes to expertise savings with a statistically significant result, making it the preferred algorithm to achieve the goal of promoting diversity while maintaining expertise in an expert recommendation process

A study of context influences in Arabic-English language translation technologies

Social and cultural context is largely missing in current language translation systems. Dictionary based systems translate terms in a source language to an equivalent term in a target language, but often the translation could be inaccurate when context is not taken into consideration, or when an equivalent term in the target language does not exist. Domain knowledge and context can be made explicit by using ontologies, and ontology utilization would enable inclusion of semantic relations to other terms, leading to translation results which is more comprehensive than a single equivalent term. It is proposed that existing ontologies in the domain should be utilized and combined by ontology merging techniques, to leverage on existing resources to form a basis ontology with contextual representation, and this can be further enhanced by using machine translation techniques on existing corpora to improve the basic ontology to append further contextual information to the knowledge base. Statistical methods in machine translation could provide automated relevance determination of these existing resources which are machine readable, and aid the human translator in establishing a domain specific knowledge base for translation. Advancements in communication and technologies has made the world smaller where people of different regions and languages need to work together and interact.The accuracy of these translations are crucial as it could lead to misunderstandings and possible conflict. While single equivalent terms in a target language can provide a gist of the meaning of a source language term, a semantic conceptualisation provided by an ontology could enable the term to be understood in the specific context that it is being used

Enforcing Customization in e-Learning Systems: an ontology and product line-based approach

In the era of e-Learning, educational materials are considered a crucial point for all the stakeholders. On the one hand, instructors aim at creating learning materials that meet the needs and expectations of learners easily and effec-tively; On the other hand, learners want to acquire knowledge in a way that suits their characteristics and preferences. Consequently, the provision and customization of educational materials to meet the needs of learners is a constant challenge and is currently synonymous with technological devel-opment. Promoting the personalization of learning materials, especially dur-ing their development, will help to produce customized learning materials for specific learners' needs. The main objective of this thesis is to reinforce and strengthen Reuse, Cus-tomization and Ease of Production issues in e-Learning materials during the development process. The thesis deals with the design of a framework based on ontologies and product lines to develop customized Learning Objects (LOs). With this framework, the development of learning materials has the following advantages: (i) large-scale production, (ii) faster development time, (iii) greater (re) use of resources. The proposed framework is the main contribution of this thesis, and is char-acterized by the combination of three models: the Content Model, which addresses important points related to the structure of learning materials, their granularity and levels of aggregation; the Customization Model, which con-siders specific learner characteristics and preferences to customize the learn-ing materials; and the LO Product Line (LOPL) model, which handles the subject of variability and creates matter-them in an easy and flexible way. With these models, instructors can not only develop learning materials, but also reuse and customize them during development. An additional contribution is the Customization Model, which is based on the Learning Style Model (LSM) concept. Based on the study of seven of them, a Global Learning Style Model Ontology (GLSMO) has been con-structed to help instructors with information on the apprentice's characteris-tics and to recommend appropriate LOs for customization. The results of our work have been reflected in the design of an authoring tool for learning materials called LOAT. They have described their require-ments, the elements of their architecture, and some details of their user inter-face. As an example of its use, it includes a case study that shows how its use in the development of some learning components.En la era del e¿Learning, los materiales educativos se consideran un punto crucial para todos los participantes. Por un lado, los instructores tienen como objetivo crear materiales de aprendizaje que satisfagan las necesidades y ex-pectativas de los alumnos de manera fácil y efectiva; por otro lado, los alumnos quieren adquirir conocimientos de una manera que se adapte a sus características y preferencias. En consecuencia, la provisión y personaliza-ción de materiales educativos para satisfacer las necesidades de los estudian-tes es un desafío constante y es actualmente sinónimo de desarrollo tecnoló-gico. El fomento de la personalización de los materiales de aprendizaje, es-pecialmente durante su desarrollo, ayudará a producir materiales de aprendi-zaje específicos para las necesidades específicas de los alumnos. El objetivo fundamental de esta tesis es reforzar y fortalecer los temas de Reutilización, Personalización y Facilidad de Producción en materiales de e-Learning durante el proceso de desarrollo. La tesis se ocupa del diseño de un marco basado en ontologías y líneas de productos para desarrollar objetos de aprendizaje personalizados. Con este marco, el desarrollo de materiales de aprendizaje tiene las siguientes ventajas: (i) producción a gran escala, (ii) tiempo de desarrollo más rápido, (iii) mayor (re)uso de recursos. El marco propuesto es la principal aportación de esta tesis, y se caracteriza por la combinación de tres modelos: el Modelo de Contenido, que aborda puntos importantes relacionados con la estructura de los materiales de aprendizaje, su granularidad y niveles de agregación, el Modelo de Persona-lización, que considera las características y preferencias específicas del alumno para personalizar los materiales de aprendizaje, y el modelo de Línea de productos LO (LOPL), que maneja el tema de la variabilidad y crea ma-teriales de manera fácil y flexible. Con estos modelos, los instructores no sólo pueden desarrollar materiales de aprendizaje, sino también reutilizarlos y personalizarlos durante el desarrollo. Una contribución adicional es el modelo de personalización, que se basa en el concepto de modelo de estilo de aprendizaje. A partir del estudio de siete de ellos, se ha construido una Ontología de Modelo de Estilo de Aprendiza-je Global para ayudar a los instructores con información sobre las caracterís-ticas del aprendiz y recomendarlos apropiados para personalización. Los resultados de nuestro trabajo se han plasmado en el diseño de una he-rramienta de autor de materiales de aprendizaje llamada LOAT. Se han des-crito sus requisitos, los elementos de su arquitectura, y algunos detalles de su interfaz de usuario. Como ejemplo de su uso, se incluye un caso de estudio que muestra cómo su empleo en el desarrollo de algunos componentes de aprendizaje.En l'era de l'e¿Learning, els materials educatius es consideren un punt crucial per a tots els participants. D'una banda, els instructors tenen com a objectiu crear materials d'aprenentatge que satisfacen les necessitats i expectatives dels alumnes de manera fàcil i efectiva; d'altra banda, els alumnes volen ad-quirir coneixements d'una manera que s'adapte a les seues característiques i preferències. En conseqüència, la provisio' i personalitzacio' de materials edu-catius per a satisfer les necessitats dels estudiants és un desafiament constant i és actualment sinònim de desenvolupament tecnològic. El foment de la personalitzacio' dels materials d'aprenentatge, especialment durant el seu desenvolupament, ajudarà a produir materials d'aprenentatge específics per a les necessitats concretes dels alumnes. L'objectiu fonamental d'aquesta tesi és reforçar i enfortir els temes de Reutilització, Personalització i Facilitat de Producció en materials d'e-Learning durant el procés de desenvolupament. La tesi s'ocupa del disseny d'un marc basat en ontologies i línia de productes per a desenvolupar objec-tes d'aprenentatge personalitzats. Amb aquest marc, el desenvolupament de materials d'aprenentatge té els següents avantatges: (i) produccio' a gran esca-la, (ii) temps de desenvolupament mes ràpid, (iii) major (re)ús de recursos. El marc proposat és la principal aportacio' d'aquesta tesi, i es caracteritza per la combinacio' de tres models: el Model de Contingut, que aborda punts im-portants relacionats amb l'estructura dels materials d'aprenentatge, la se-ua granularitat i nivells d'agregació, el Model de Línia de Producte, que ges-tiona el tema de la variabilitat i crea materials d'aprenentatge de manera fàcil i flexible. Amb aquests models, els instructors no solament poden desenvolu-par materials d'aprenentatge, sinó que també poden reutilitzar-los i personalit-zar-los durant el desenvolupament. Una contribucio' addicional és el Model de Personalitzacio', que es basa en el concepte de model d'estil d'aprenentatge. A partir de l'estudi de set d'ells, s'ha construït una Ontologia de Model d'Estil d'Aprenentatge Global per a ajudar als instructors amb informacio' sobre les característiques de l'aprenent i recomanar els apropiats per a personalitzacio'. Els resultats del nostre treball s'han plasmat en el disseny d'una eina d'autor de materials d'aprenentatge anomenada LOAT. S'han descrit els seus requi-sits, els elements de la seua arquitectura, i alguns detalls de la seua interfície d'usuari. Com a exemple del seu ús, s'inclou un cas d'estudi que mostra com és el desenvolupament d'alguns components d'aprenentatge.Ezzat Labib Awad, A. (2017). Enforcing Customization in e-Learning Systems: an ontology and product line-based approach [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90515TESI

A Novel Adaptation Model for E-Learning Recommender Systems Based on Student’s Learning Style

In recent years, a substantial increase has been witnessed in the use of online learning resources by learn- ers. However, owing to an information overload, many find it difficult to retrieve appropriate learning resources for meeting learning requirements. Most of the existing systems for e-learning make use of a “one-size-fits-all” approach, thus providing all learners with the same content. Whilst recommender systems have scored notable success in the e-commerce domain, they still suffer from drawbacks in terms of making the right recommendations for learning resources. This can be attributed to the differences among learners’ preferences such as varying learning styles, knowledge levels and sequential learning patterns. Hence, to identify the needs of an individual student, e-learning systems that can build profiles of student preferences are required. In addition, changing students’ preferences and multidimensional attributes of the course content are not fully considered simultaneously. It is by failing to review these issues that existing recommendation algorithms often give inaccurate recommendations. This thesis focuses on student learning styles, with the aim of dynamically tailoring the learning process and course content to meet individual needs. The proposed Ubiquitous LEARNing (ULEARN) system is an adaptive e-learning recommender system geared towards providing a personalised learning environ- ment, which ensures that course learning objects are in line with the learner’s adaptive profile. This thesis delivers four main contributions: First, an innovative algorithm which dynamically reduces the number of questions in the Felder-Silverman Learning Styles (FSLSM) questionnaire for the purpose of initialising student profiles has been proposed. The second contribution comprises examining the accuracy of various similarity metrics so as to select the most suitable similarity measurements for learning objects recommendation algorithm. The third contribution includes an Enhanced Collaboration Filtering (ECF) algorithm and an Enhanced Content-Based Filtering (ECBF) algorithm, which solves the issues of cold-start and data sparsity in- herent to the traditional Collaborative Filtering (CF) and the traditional Content-based Filtering (CBF), respectively. Moreover, these two new algorithms have been combined to create a new Enhanced Hybrid Filtering (EHF) algorithm that recommends highly accurate personalised learning objects on the basis of the stu- dents’ learning styles. The fourth contribution is a new algorithm that tracks patterns of student learning behaviours and dynam- ically adapts the student learning style accordingly. The ULEARN recommendation system was implemented with Visual Studio in C++ and Windows Pre- sentation Foundation (WPF) for the development of the Graphical User Interface (GUI). The experimental results revealed that the proposed algorithms have achieved significant improvements in student’s profile adaptation and learning objects recommendation in contrast with strong benchmark models. Further find- ings from experiments indicated that ULEARN can provide relevant learning object recommendations based on students’ learning styles with the overall students’ satisfaction at almost 90%. Furthermore, the results showed that the proposed system is capable of mitigating the problems data sparsity and cold-start, thereby improving the accuracy and reliability of recommendation of the learning object. All in all, the ULEARN system is competent enough to support educational institutions in recommending personalised course content, improving students’ performance as well as promoting student engagement.Arab academy for science technology & maritime transpor

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Fair and Diverse Group Formation Based on Multidimensional Features

The goal of group formation is to build a team to accomplish a specific task. Algorithms are being developed to improve the team\u27s effectiveness so formed and the efficiency of the group selection process. However, there is concern that team formation algorithms could be biased against minorities due to the algorithms themselves or the data on which they are trained. Hence, it is essential to build fair team formation systems that incorporate demographic information into the process of building the group. Although there has been extensive work on modeling individuals’ expertise for expert recommendation and/or team formation, there has been relatively little prior work on modeling demographics and incorporating demographics into the group formation process. We propose a novel method to represent experts’ demographic profiles based on multidimensional demographic features. Moreover, we introduce three diversity ranking algorithms that form a group by considering demographic features along with the minimum required skills. Unlike many ranking algorithms that consider one Boolean demographic feature (e.g., gender or race), our diversity ranking algorithms consider multiple demographic features simultaneously. Finally, we introduce a fair team formation algorithm that balances each candidate\u27s demographic information and expertise. We evaluate our proposed algorithms using real datasets based on members of a computer science program committee. The result shows that our algorithms form a program committee that is more diverse with an acceptable loss in utility