Empowering Recommendation Technologies Through Argumentation

Chesñevar, Carlos Iván; Maguitman, Ana Gabriela; González, María Paula

doi:10.1007/978-0-387-98197-0_20

Cited by 45 publications

(51 citation statements)

References 28 publications

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“…hybrid recommenders [6]). However, [7] has stated the inability of current recommender systems to use the large amount of qualitative data available online to empower recommendations. Usually, recommender systems do not provide an explanation about the reasoning process that has been followed to come up with speci c recommendations.…”

Section: Introductionmentioning

confidence: 99%

“…In order to overcome these problems, it is necessary to embed a social layer in current recommender approaches, taking into account aspects such as the generation of arguments that support recommendations, reputation and trust. Therefore, there are a number of open challenges for the development of a new generation of recommender systems [7], such as exposing underlying assumptions behind recommendations, approaching trust and trustworthiness from the perspective of backing recommendations and providing rationally compelling arguments for recommendations. Our work involves a contribution in these areas, presenting a persuasive social recommendation system for recipe recommendation in a social network.…”

Section: Introductionmentioning

confidence: 99%

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receteame.com: A Persuasive Social Recommendation System

Palanca

Heras

Botti

et al. 2014

Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

receteame.com: A Persuasive Social Recommendation System

Palanca

Heras

Botti

et al. 2014

Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

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“…Recently, some argument-based recommender systems and recommendation techniques have been proposed to recommend music [16], news [17], movies [18], or restaurants [19], to perform content-based web search [20] or to formalize and structure user opinions in online recommender systems [21]. Among them, we share the approach of the movie recommender system based on defeasible logic programming proposed in [22].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, even when users already know the recommendations presented, the latter work demonstrated that they prefer recommender systems that are able to justify their suggestions. Thus, what is understood as a good recommendation is changing from the one that minimises some error evaluation to the one that is really able to persuade people and make them happier.Recently, some argument-based recommender systems and recommendation techniques have been proposed to recommend music [16], news [17], movies [18], or restaurants [19], to perform content-based web search [20] or to formalize and structure user opinions in online recommender systems [21]. Among them, we share the approach of the movie recommender system based on defeasible logic programming proposed in [22].…”

mentioning

confidence: 99%

“…In educational domains, argumentation theory and tools have a large history of successful applications, specially to teach critical thinking skills in law courses [23]. However, to the best of our knowledge, the application of argumentation theory to enhance ERS is a new area of research.There are a number of open challenges for the application of argumentation theory to recommender systems [20], such as exposing underlying assumptions behind recommendations, approaching trust and trustworthiness from the perspective of backing recommendations and providing rationally compelling arguments for recommendations. Our work involves a contribution in this latter area.…”

mentioning

confidence: 99%

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Argumentation-Based Hybrid Recommender System for Recommending Learning Objects

Rodríguez

Heras

Palanca

et al. 2016

Multi-Agent Systems and Agreement Technologies

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Abstract. 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 hybidization 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 hybrid recommendation method based on argumentation theory that combines content-based, collaborative and knowledge-based recommendation techniques and provides the students with those objects for which the system is able to generate more arguments to justify their suitability. This method has been tested by using a database with real data about students and learning objects, getting promising results. MotivationAccording to the IEEE, a learning object (LO) can be defined as a digital entity involving educational design characteristics. Each LO can be used, reused or referenced during computer-supported learning processes, aiming at generating knowledge and competences based on student's needs [1]. LOs have functional requirements such as accessibility, reuse, and interoperability. The concept of LO requires understanding of how people learn, since this issue directly affects the LO design in each of its three dimensions: pedagogical, didactic, and technological [2]. In addition, LOs have metadata that describe and identify the educational resources involved and facilitate their searching and retrieval. Learning ObjectsCorresponding author.Repositories (LORs), composed of thousands of LOs, can be defined as specialized digital libraries storing several types of heterogeneous resources. LORs are currently being used in various e-learning environments and belong mainly to educational institutions [2,3]. Also, federations of LORs provide educational applications to search, retrieve and access specific LO contents available in any LOR [4]. Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences [5]. In virtual learning environments, Educational Recommender Systems (ERS) deliver LOs according to the student's characteristics, preferences and learning needs. In order to improve recommendations, ERS must perform feedback processes and implement mechanisms that enable them to obtain a large amount of information about users and how they use the LOs. ERS can be classified into s...

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A Content Management System for Chatbots

Galitsky

2019

Developing Enterprise Chatbots

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Empowering Recommendation Technologies Through Argumentation

Cited by 45 publications

References 28 publications

receteame.com: A Persuasive Social Recommendation System

receteame.com: A Persuasive Social Recommendation System

Argumentation-Based Hybrid Recommender System for Recommending Learning Objects

A Content Management System for Chatbots

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