Argument-based generation and explanation of recommendations
- 13 September 2021
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM) in Fifteenth ACM Conference on Recommender Systems
Abstract
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 processes.Keywords
Funding Information
- Ministerio de Ciencia e Innovación (PID2019-108965GB-I00)
This publication has 35 references indexed in Scilit:
- Recommender systems based on user reviews: the state of the artUser Modelling and User-Adapted Interaction, 2015
- Show Me Your Evidence - an Automatic Method for Context Dependent Evidence DetectionPublished by Association for Computational Linguistics (ACL) ,2015
- Argument-based mixed recommenders and their application to movie suggestionExpert Systems with Applications, 2014
- A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATIONInternational Journal on Artificial Intelligence Tools, 2013
- Argumentation miningArtificial Intelligence and Law, 2011
- Argument-based critics and recommenders: A qualitative perspective on user support systemsData & Knowledge Engineering, 2006
- Automatic legal text summarisationPublished by Association for Computing Machinery (ACM) ,2005
- On the Acceptability of Arguments in Bipolar Argumentation FrameworksLecture Notes in Computer Science, 2005
- Defeasible logic programming: an argumentative approachTheory and Practice of Logic Programming, 2004
- A personal news agent that talks, learns and explainsPublished by Association for Computing Machinery (ACM) ,1999