Muthusamy Chelliah
ABSTRACT
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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Index Terms
- Product Recommendations Enhanced with Reviews
Recommendations
EX3: Explainable Attribute-aware Item-set Recommendations
RecSys '21: Proceedings of the 15th ACM Conference on Recommender SystemsExisting recommender systems in the e-commerce domain primarily focus on generating a set of relevant items as recommendations; however, few existing systems utilize underlying item attributes as a key organizing principle in presenting recommendations ...
Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis
Advances in Social MediaThe motivation of collaborative filtering CF comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we ...
Generating virtual ratings from chinese reviews to augment online recommendations
Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in contextCollaborative filtering (CF) recommenders based on User-Item rating matrix as explicitly obtained from end users have recently appeared promising in recommender systems. However, User-Item rating matrix is not always available or very sparse in some web ...
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