Natural Language Justifications for Recommender Systems Exploiting Text Summarization and Sentiment Analysis

This paper reports and summarizes the methodology presented in [16] and accepted for publication at ACM RecSys 2019. In this work we present a methodology to justify recommendations that relies on the information extracted from users’ reviews discussing the available items. The intuition behind the approach is to conceive the justification as a summary of the most relevant and distinguishing aspects of the item, automatically obtained by analyzing its reviews. To this end, we designed a pipeline of natural language processing techniques including aspect extraction, sentiment analysis and text summarization to gather the reviews, process the relevant excerpts, and generate a unique synthesis presenting the main characteristics of the item. Such a summary is finally presented to the target user as a justification of the received recommendation. In the experimental evaluation we carried out a user study in the movie domain (N=141) and the results showed that our approach is able to make the recommendation process more transparent, engaging and trustful for the users. Moreover, the proposed method also beat another review-based explanation technique, thus confirming the validity of our intuition. 1 Background and Motivations A research line which is recently emerging in the context of the so-called Data Explosion [19] regards the analysis and the exploitation of the information extracted from user-generated content and, in particular, from users’ reviews. These data are interesting from both a quantitative and a qualitative point of view, suffice it to say that more than 700 million reviews and opinions are available on TripAdvisor2. The positive impact of the information extracted from users’ reviews and from user-generated content has been already acknowledged in several scenarios [1,9,14,15]: as an example, review-aware recommendation models tend to beat classical recommendation algorithms [3,6,7]. 1 https://recsys.acm. org/recsys19/ 2 https://tripadvisor.mediaroom. com/us Copyright c © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 64 Authors Suppressed Due to Excessive Length However, differently from most of the current literature, in this work we investigated the impact of the information conveyed by users’ reviews in a different scenario, that is to say, the task of justifying a suggestion returned by a recommendation algorithm. This topic is particularly relevant due to the recent regulations in the area, as the General Data Protection Regulation3 (GDPR), which emphasized the users’ right to explanation. Moreover, many studies showed that a higher transparency leads to a higher trust [20] and to a higher acceptance of the recommendations [4]. In a nutshell, our methodology relies on the idea that an effective justification can be conceived as a summary of relevant and distinguishing aspects of the item, automatically obtained by analyzing its reviews. To this end, we designed a pipeline of natural language processing techniques to obtain and process reviews excerpts and to generate a unique synthesis presenting the main characteristics of the item, which is finally presented to the target user as justification of the received recommendation. It is worth to note that we preferred to use the term justification over explanation, even if they are often used as synonyms. This choice follows the definition provided in [2], where it is stated that a justification explains why a decision is a good one, without explaining exactly how it was made, while an explanation is related to the concept of interpretability, that is to say, if the internal mechanisms as well as the process carried out by an algorithm can be understood by a human. Accordingly, the strategy we implemented is closer to the idea of justification, since our methodology is more devoted to describe why a user would be interested in the item, in order to make a more informed decision about consuming the item or not. For a discussion of the literature in the area we suggest to refer to the original paper [16]. For the sake of shortness, we can state that the distinguishing aspects of the current work lie in: – The use of text summarization techniques to generate natural language explanations; – The use of aspect extraction methods to automatically discover relevant and distinguishing aspects of the item, discussed in user reviews. In the following, we describe the main building blocks of our methodology and we will show the results emerging from the user study in the movie domain. Finally, we will discuss the main outcomes of this work, by also sketching some ideas for future research.