  The review-based recommender systems are commonly utilized to measure users
preferences towards different items. In this paper, we focus on addressing
three main problems existing in the review-based methods. Firstly, these
methods suffer from the class-imbalanced problem where rating levels with lower
proportions will be ignored to some extent. Thus, their performance on
relatively rare rating levels is unsatisfactory. As the first attempt in this
field to address this problem, we propose a flexible dual-optimizer model to
gain robustness from both regression loss and classification loss. Secondly, to
address the problem caused by the insufficient contextual information
extraction ability of word embedding, we first introduce BERT into the
review-based method to improve the performance of the semantic analysis.
Thirdly, the existing methods ignore the feature information of the
time-varying user preferences. Therefore, we propose a time-varying feature
extraction module with bidirectional long short-term memory and multi-scale
convolutional neural network. Afterward, an interaction component is proposed
to further summarize the contextual information of the user-item pairs. To
verify the effectiveness of the proposed TADO, we conduct extensive experiments
on 23 benchmark datasets selected from Amazon Product Reviews. Compared with
several recently proposed state-of-the-art methods, our model obtains
significant gain over ALFM, MPCN, and ANR averagely with 20.98\%, 9.84\%, and
15.46\%, respectively. Further analysis proves the necessity of jointly using
the proposed components in TADO.
