Explainable recommendation is a technique that combines prediction and
generation tasks to produce more persuasive results. Among these tasks, textual
generation demands large amounts of data to achieve satisfactory accuracy.
However, historical user reviews of items are often insufficient, making it
challenging to ensure the precision of generated explanation text. To address
this issue, we propose a novel model, ERRA (Explainable Recommendation by
personalized Review retrieval and Aspect learning). With retrieval enhancement,
ERRA can obtain additional information from the training sets. With this
additional information, we can generate more accurate and informative
explanations. Furthermore, to better capture users' preferences, we incorporate
an aspect enhancement component into our model. By selecting the top-n aspects
that users are most concerned about for different items, we can model user
representation with more relevant details, making the explanation more
persuasive. To verify the effectiveness of our model, extensive experiments on
three datasets show that our model outperforms state-of-the-art baselines (for
example, 3.4% improvement in prediction and 15.8% improvement in explanation
for TripAdvisor)