T-RECS: a Transformer-based Recommender Generating Textual Explanations and Integrating Unsupervised Language-based Critiquing

05/22/2020 ∙ by Diego Antognini, et al. ∙ 0

Supporting recommendations with personalized and relevant explanations increases trust and perceived quality, and helps users make better decisions. Prior work attempted to generate a synthetic review or review segment as an explanation, but they were not judged convincing in evaluations by human users. We propose T-RECS, a multi-task learning Transformer-based model that jointly performs recommendation with textual explanations using a novel multi-aspect masking technique. We show that human users significantly prefer the justifications generated by T-RECS than those generated by state-of-the-art techniques. At the same time, experiments on two datasets show that T-RECS slightly improves on the recommendation performance of strong state-of-the-art baselines. Another feature of T-RECS is that it allows users to react to a recommendation by critiquing the textual explanation. The system updates its user model and the resulting recommendations according to the critique. This is based on a novel unsupervised critiquing method for single- and multi-step critiquing with textual explanations. Experiments on two real-world datasets show that T-RECS is the first to obtain good performance in adapting to the preferences expressed in multi-step critiquing.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 13

page 19

page 21

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.