Learning product representations for generating reviews for cold products

Abstract

Existing work in the literature have shown that the number and quality of product ratings and reviews have a direct correlation with the product purchase rates in online e-commerce portals. However, the majority of the products on e-commerce portals do not have any ratings or reviews and are known as cold products (∼90% of products on Amazon are cold). As such, there has been growing interest in generating reviews for cold products by selectively transferring reviews from other similar yet warm products. Our work in this paper focuses on this specific problem and generates reviews for cold products through review selection. Similar to existing work in the literature, our work assumes a relationship between product attribute-values and the reviews that products receive. However, unlike the literature, our method (1) is not restricted to the exact surface form of a product attribute name; and, (2) can distinguish between the same attribute expressed in different forms. We achieve these two important characteristics by proposing methods to learn neural product representations that capture the semantics of product attribute-values as they relate to user reviews. More specifically, our work offers (i) an approach to learn neural representations of product attribute-values within a shared embedding space as product reviews; (ii) a weighted composition strategy to develop product representations from the representation of its attributes; and, (iii) a review selection method that selects relevant reviews for the composed product representation within the neural embedding space. We show through our extensive experiments on five datasets consisting of products from CNET.com and movies from rottentomatoes.com that our method is able to show stronger performance compared to several baselines on ROUGE-2 metrics.