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.
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.
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Supporting recommendations with personalized and relevant explanations increases
Using personalized explanations to support recommendations has been shown to increase
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, 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
. 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
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the justifications generated by T-RECS than those generated
these explanations over those produced
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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
Our work's most important innovation
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: removing (symmetrically adding) certain aspects they dislike or that are no longer relevant (symmetrically that are of interest)
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T-RECS
our system
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obtain
achieve