4 research outputs found
Context-aware Helpfulness Prediction for Online Product Reviews
Modeling and prediction of review helpfulness has become more predominant due
to proliferation of e-commerce websites and online shops. Since the
functionality of a product cannot be tested before buying, people often rely on
different kinds of user reviews to decide whether or not to buy a product.
However, quality reviews might be buried deep in the heap of a large amount of
reviews. Therefore, recommending reviews to customers based on the review
quality is of the essence. Since there is no direct indication of review
quality, most reviews use the information that ''X out of Y'' users found the
review helpful for obtaining the review quality. However, this approach
undermines helpfulness prediction because not all reviews have statistically
abundant votes. In this paper, we propose a neural deep learning model that
predicts the helpfulness score of a review. This model is based on
convolutional neural network (CNN) and a context-aware encoding mechanism which
can directly capture relationships between words irrespective of their distance
in a long sequence. We validated our model on human annotated dataset and the
result shows that our model significantly outperforms existing models for
helpfulness prediction.Comment: Published as a proceeding paper in AIRS 201