Product reviews written by the crowd on e-commerce shopping websites have become a critical information source for making purchasing decisions. An important challenge, however, is that the vast majority of products (e.g., of products on amazon.com) do not receive enough attention and lack sufficient reviews by the users; hence, they constitute the so-called cold products. One solution to address cold products, which has already been studied in the literature, is to generate reviews for these products by sampling review sentences from closely related warm products. Our method proposed in this paper is specifically focused on such a solution. While a majority of the works in the literature rely on product specification similarity to identify relevant reviews that can be used for review sentence selection, our work differs in that it not only employs product specification similarity but also employs product-review, product-user, and user-review interactions when determining the suitability of a review sentence to be selected. More specifically, the contributions of our work can be enumerated as follows: (1) We propose that the selection of review sentences from other products should not only consider product-product similarity but also consider product-review, user-review, and user-user relationships. As such, we show how neural graph embeddings can be used to encode product, user, and review information into an attributed heterogeneous graph representation based on which similarities can be calculated. (2) We further propose how review relevance and importance can be considered using graph traversal to select appropriate review sentences for a given cold product. (3) Finally, we systematically compare the performance of our work with those of several state-of-the-art baselines on five datasets collected from CNET.com and rottentomatoes.com with different characteristics from both quantitative (e.g., the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics) and qualitative aspects and show how our proposed approach was able to provide statistically significantly improved performance over various strong baselines.

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