  Using personalized explanations to support recommendations has been shown to
increase trust and perceived quality. However, to actually obtain better
recommendations, there needs to be a means for users to modify the
recommendation criteria by interacting with the explanation. We present a novel
technique using aspect markers that learns to generate personalized
explanations of recommendations from review texts, and we show that human users
significantly prefer these explanations over those produced by state-of-the-art
techniques. Our work's most important innovation is that it allows users to
react to a recommendation by critiquing the textual explanation: removing
(symmetrically adding) certain aspects they dislike or that are no longer
relevant (symmetrically that are of interest). 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 our system is the first to achieve good performance in adapting to
the preferences expressed in multi-step critiquing.
