User-written product reviews contain rich information about user preferences for product features and provide helpful explanations that are often used by shoppers to make their purchase decisions. E-commerce recommender systems can benefit enormously by also exploiting experiences of multiple customers captured in product reviews. In this tutorial, we present a range of techniques that allow recommender systems in e-commerce websites to take full advantage of reviews. This includes text mining methods for feature-specific sentiment analysis of products, topic models and distributed representations that bridge the vocabulary gap between user reviews and product descriptions. We present recommender algorithms that use review information to address the cold-start problem and generate recommendations with explanations. We discuss examples and experiences from an online marketplace (i.e., Flipkart).
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Chelliah et al. (2017) studied this question.
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