Argumentation-enabled interest-based personalised recommender system

Bedi, Punam; Vashisth, Pooja

doi:10.1080/0952813x.2014.924586

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…The use of argumentation-based reasoning engines provides useful advantages in the implementation in a variety of user support systems such as expert systems, systems for automated negotiation, recommender systems and decision support systems (see for instance, Amgoud & Prade (2009);Chesñevar et al (2009);Ferretti et al (2008); Monteserin & Amandi (2011); Bedi & Vashisth (2015); Briguez et al (2013Briguez et al ( , 2014). As pointed out in the literature, having different argument comparison criteria available introduces an interesting degree of flexibility in these systems; however, in most of the proposed systems, the comparison criterion is either a fixed component or, if there exist multiplicity of criteria at the user disposal, there exist no way of changing it once a criterion is chosen.…”

Section: Discussionmentioning

confidence: 99%

“…The reason behind this observation is that argumentation can qualitatively deal with conflicting domain information, and its dialectical reasoning nature can provide a clear explanation of the recommendation without further cost as shown in García et al (2013). Such advantages have motivated the development of several argumentation based recommender and decision support systems: Bedi & Vashisth (2011, 2015; Briguez et al (2013Briguez et al ( , 2014; Chesñevar et al (2009) ;Amgoud & Prade (2009).…”

Section: Introductionmentioning

confidence: 86%

“…Several works of the literature Bedi & Vashisth (2011);Heras et al (2013); Rajpal et al (2014); Budzynska et al (2014) have already applied argumentation methods to recommendation systems. Recently, Bedi & Vashisth (2015), have presented an interest-based recommender system (IBRS) for personalization of recommendations; IBRS considers user's preference and employs argumentation to generate suggestions. The use of argumentation allows these systems to reason about the underlying interests behind user's personal preferences, and helps to resolve conflicts (using preferences) between the recommendations with convincing arguments.…”

Section: Work On Recommendation Systemsmentioning

confidence: 99%

“…However, in most of current recommender systems, the comparison criterion is either fixed (i.e., cannot be changed) or codified within the arguments (i.e., the arguments are built to satisfy a particular criteria) Chesñevar et al (2009) ;Bedi & Vashisth (2011, 2015; Briguez et al (2013). In others approaches, as in Deagustini et al (2012); Briguez et al (2014), when several criteria are available, the system does not provide a formal way to select or change the comparison criteria that should be used.…”

Section: Introductionmentioning

confidence: 99%

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Improving argumentation-based recommender systems through context-adaptable selection criteria

Teze

Gottifredi

García

et al. 2015

Expert Systems with Applications

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Recommender Systems based on argumentation represent an important proposal where the recommendation is supported by qualitative information. In these systems, the role of the comparison criterion used to decide between competing arguments is paramount and the possibility of using the most appropriate for a given domain becomes a central issue; therefore, an argumentative recommender system that offers an interchangeable argument comparison criterion provides a significant ability that can be exploited by the user. However, in most of current recommender systems, the argument comparison criterion is either fixed, or codified within the arguments. In this work we propose a formalization of context-adaptable selection criteria that enhances the argumentative reasoning mechanism. Thus, we do not propose of a new type of recommender system; instead we present a mechanism that expand the capabilities of existing argumentation-based recommender systems. More precisely, our proposal is to provide a way of specifying how to select and use the most appropriate argument comparison criterion effecting the selection on the user's preferences, giving the possibility of programming, by the use of conditional expressions, which argument preference criterion has to be used in each particular situation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 86%

Section: Work On Recommendation Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving argumentation-based recommender systems through context-adaptable selection criteria

Teze

Gottifredi

García

et al. 2015

Expert Systems with Applications

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show abstract

“…Following this approach based on a MAS architecture, the recommendations obtained using an RS could be improved using an argumentation framework amongst agents (Bedi & Vashisth, ; Rodríguez, Heras, Palanca, Duque, & Julián, ; Teze, Gottifredi, García, & Simari, ). The aim of an argumentation framework is that agents propose a solution based on their knowledge and argument with each other, each trying to defend and convince the other agents of their own solution until an agreement is reached.…”

Section: Introductionmentioning

confidence: 99%

Hybrid job offer recommender system in a social network

et al. 2019

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Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.

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Enabling a Bot with Understanding Argumentation and Providing Arguments

Galitsky

2019

Developing Enterprise Chatbots

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Argumentation-enabled interest-based personalised recommender system

Cited by 14 publications

References 30 publications

Improving argumentation-based recommender systems through context-adaptable selection criteria

Improving argumentation-based recommender systems through context-adaptable selection criteria

Hybrid job offer recommender system in a social network

Enabling a Bot with Understanding Argumentation and Providing Arguments

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