ASSURE

Argument-Based Decision Support for Recommender Systems

Argumentative statements contained in user-generated texts such as online product reviews can significantly facilitate a user’s decision. Recommender systems aim at alleviating the user’s decision problem by suggesting items the user is likely interested in, but do not exploit the potential of reasoned arguments given for or against a certain item or its properties. The overall objective of the ASSURE project is to make use of arguments embedded in online reviews to significantly improve the quality and transparency of recommendations given by the system, and to provide users with a much higher level of interactive control over the recommendation process than is currently the case.

ASSURE aims at advancing the state of the art in several respects: Firstly, we will develop novel methods for extracting arguments from the typically informal texts found in user reviews. We will further enrich the arguments with annotations of how specific and how emotionally intense they are.

Secondly, we will combine the extracted arguments and the additional annotations with user ratings and other item-related data in an integrated user and item model to improve the effectiveness of recommender algorithms. This model will also provide a basis for developing novel techniques through which users can interactively explore, filter, or weight different arguments, as well as other data, to control how recommendations are generated. Thirdly, we will develop methods for providing users with personalized, argument-based explanations of the items recommended. A further important outcome of the project will be a dataset of unprecedented quality and size that is annotated on different layers regarding argumentation. Such a dataset is a prerequisite for further research on argumentation in the context of recommending, and will be suited for use in shared tasks that form part of the priority program.

The project ASSURE forms part of the DFG Priority Programme Robust Argumentation Machines and is carried out in cooperation with Prof. Torsten Zesch (Language Technology Lab, University of Duisburg-Essen).