Mining Information for the Cold-Item Problem

One of the strong points of E-commerce websites is that they are often abundant with product reviews from consumers who experienced the products and testify to the usefulness of the products or otherwise. These reviews are helpful for consumers to optimize their purchasing decisions. However, while popular products receive many reviews, many other products do not have an adequate number of reviews leading to the cold item problem. In this proposal, we propose a solution outline for the cold item problem by automatically generating reviews and predicting ratings for the cold products from available reviews of similar products in e-commerce websites as well as users' opinion shared in the microblogging platforms such as Twitter. We propose a framework to build a formal semantic representation of products from unstructured product descriptions, user reviews as well as user ratings. Such presentations assist us to measure product similarity and relatedness in a accurate and cost-effective way. Besides, we propose a model to generate additional reviews for a cold product by mining users' posts shared on medium such as Twitter and transfer them to the e-commerce website. Preliminary experiments show promising results in finding products similar to the cold products.