  Recommender systems are designed to help mitigate information overload users
experience during online shopping. Recent work explores neural language models
to learn user and item representations from user reviews and combines such
representations with rating information. Most existing convolutional-based
neural models take pooling immediately after convolution and loses the
interaction information between the latent dimension of convolutional feature
vectors along the way. Moreover, these models usually take all feature vectors
at higher levels as equal and do not take into consideration that some features
are more relevant to this specific user-item context. To bridge these gaps,
this paper proposes a convolutional quantum-like language model with
mutual-attention for rating prediction (ConQAR). By introducing a quantum-like
density matrix layer, interactions between latent dimensions of convolutional
feature vectors are well captured. With the attention weights learned from the
mutual-attention layer, final representations of a user and an item absorb
information from both itself and its counterparts for making rating prediction.
Experiments on two large datasets show that our model outperforms multiple
state-of-the-art CNN-based models. We also perform an ablation test to analyze
the independent effects of the two components of our model. Moreover, we
conduct a case study and present visualizations of the quantum probabilistic
distributions in one user and one item review document to show that the learned
distributions capture meaningful information about this user and item, and can
be potentially used as textual profiling of the user and item.
