Exploiting Distributional Semantics Models for Natural Language Context-aware Justifications for Recommender Systems
Authors: Giuseppe Spillo, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro (Università di Bari Aldo Moro, 2020)
Topic: Chatbots and Dialogue Systems

Abstract
In this paper we present a methodology to generate context-aware natural language justifications supporting the suggestions produced by a recommendation algorithm. Our approach relies on a natural language processing pipeline that exploits distributional semantics models to identify the most relevant aspects for each different context of consumption of the item. Next, these aspects are used to identify the most suitable pieces of information to be combined in a natural language justification. As information source, we used a corpus of reviews. Accordingly, our justifications are based on a combination of reviews' excerpts that discuss the aspects that are particularly relevant for a certain context. In the experimental evaluation, we carried out a user study in the movies domain in order to investigate the validity of the idea of adapting the justifications to the different contexts of usage. As shown by the results, all these claims were supported by the data we collected.

Introduction
Recommender Systems (RSs) AUTHOR are now recognised as a very effective mean to support the users in decision-making tasks AUTHOR. However, as the importance of such technology in our everyday lives grows, it is fundamental that these algorithms support each suggestion through a justification that allows the user to understand the internal mechanisms of the recommendation process and to more easily discern among the available alternatives. To this end, several attempts have been recently devoted to investigate how to introduce explanation facilities in RSs AUTHOR and to identify the most suitable explanation styles AUTHOR. Despite such a huge research effort, none of the methodologies currently presented in literature diversifies the justifications based on the different contextual situations in which the item will be consumed. This is a clear issue, since context plays a key role in every decision-making task, and RSs are no exception. Indeed, as the mood or the company (friends, family, children) can direct the choice of the movie to be watched, so a justification that aims to convince a user to enjoy a recommendation should contain different concepts depending on whether the user is planning to watch a movie with her friends or with her children. In this paper we fill in this gap by proposing an approach to generate a context-aware justification that supports a recommendation. Our methodology exploits distributional semantics models AUTHOR to build a term-context matrix that encodes the importance of terms and concepts in each context of consumption. Such a matrix is used to obtain a vector space representation of each context, which is in turn used to identify the most suitable pieces of information to be combined in a justification. As information source, we used a corpus of reviews. Accordingly, our justifications are based on a combination of reviews' excerpts that discuss with a positive sentiment the aspects that are particularly relevant for a certain context. Beyond its context-aware nature, another distinctive trait of our methodology is the fact that we generate post-hoc justifications that are completely independent from the underlying recommendation models and completely separated from the step of generating the recommendations. To sum up, we can summarize the contributions of the article as follows: (i) we propose a methodology based on distributional semantics models and natural language processing to automatically learn a vector space representation of the different contexts in which an item can be consumed; (ii) We design a pipeline that exploits distributional semantics models to generate context-aware natural language justifications supporting the suggestions returned by any recommendation algorithm; The rest of the paper is organized as follows: first, in Section 2 we provide an overview of related work. Next, Section 3 describes the main components of our workflow and Section 4 discusses the outcomes of the experimental evaluation. Finally, conclusions and future work of the current research are provided in Section 5.

Conclusion
In this paper we presented a methodology that exploits DSMs to build post-hoc context-aware natural language justifications supporting the suggestions generated by a RS. The hallmark of this work is the diversification of the justifications based on the different contextual settings in which the items will be consumed, which is a new research direction in the area. As shown in our experiments, our justifications were largely preferred by users. This confirms the effectiveness of our approach and paves the way to several future research directions, such as the definition of personalized justification as well as the generation of hybrid justifications that combine elements gathered from usergenerated content (as the reviews) with descriptive characteristics of the items. Finally, we will also evaluate the use of ontologies and rules (Laera et al., 2004) in order to implement reasoning mechanisms to better identify the most relevant aspects in the reviews.