  Modern E-commerce websites contain heterogeneous sources of information, such
as numerical ratings, textual reviews and images. These information can be
utilized to assist recommendation. Through textual reviews, a user explicitly
express her affinity towards the item. Previous researchers found that by using
the information extracted from these reviews, we can better profile the users'
explicit preferences as well as the item features, leading to the improvement
of recommendation performance. However, most of the previous algorithms were
only utilizing the review information for explicit-feedback problem i.e. rating
prediction, and when it comes to implicit-feedback ranking problem such as
top-N recommendation, the usage of review information has not been fully
explored. Seeing this gap, in this work, we investigate the effectiveness of
textual review information for top-N recommendation under E-commerce settings.
We adapt several SOTA review-based rating prediction models for top-N
recommendation tasks and compare them to existing top-N recommendation models
from both performance and efficiency. We find that models utilizing only review
information can not achieve better performances than vanilla implicit-feedback
matrix factorization method. When utilizing review information as a regularizer
or auxiliary information, the performance of implicit-feedback matrix
factorization method can be further improved. However, the optimal model
structure to utilize textual reviews for E-commerce top-N recommendation is yet
to be determined.
