Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency

• URL: http://arxiv.org/abs/2205.01759v1
• Date: Tue, 3 May 2022 20:04:32 GMT
• Title: Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency
• Authors: I\~nigo L\'opez-Riob\'oo Botana (1), Ver\'onica Bol\'on-Canedo (1), Bertha Guijarro-Berdi\~nas (1), Amparo Alonso-Betanzos (1) ((1) University of A Coru\~na - Research Center on Information and Communication Technologies (CITIC))
• Abstract summary: We have focused on the TripAdvisor platform, considering the applicability to other dyadic data contexts. Our aim is to represent and explain pairs (user, restaurant) established by agents (e.g., a recommender system or a paid promotion mechanism) so that personalisation is taken into account. We propose the PTER (Personalised TExt-based Reviews) model. We predict, from the available reviews for a given restaurant, those that fit to the specific user interactions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: There are many contexts where dyadic data is present. Social networking is a well-known example, where transparency has grown on importance. In these contexts, pairs of items are linked building a network where interactions play a crucial role. Explaining why these relationships are established is core to address transparency. These explanations are often presented using text, thanks to the spread of the natural language understanding tasks. We have focused on the TripAdvisor platform, considering the applicability to other dyadic data contexts. The items are a subset of users and restaurants and the interactions the reviews posted by these users. Our aim is to represent and explain pairs (user, restaurant) established by agents (e.g., a recommender system or a paid promotion mechanism), so that personalisation is taken into account. We propose the PTER (Personalised TExt-based Reviews) model. We predict, from the available reviews for a given restaurant, those that fit to the specific user interactions. PTER leverages the BERT (Bidirectional Encoders Representations from Transformers) language model. We customised a deep neural network following the feature-based approach. The performance metrics show the validity of our labelling proposal. We defined an evaluation framework based on a clustering process to assess our personalised representation. PTER clearly outperforms the proposed adversary in 5 of the 6 datasets, with a minimum ratio improvement of 4%.

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