A recommender System for Restaurant Reviews Based on Consumer Segment

Previous research shows that consumers use online reviews for a variety of reasons. For many products / services, there are a large number of reviews which makes it difficult for consumers to decide which reviews to pay attention to. Hence, previous research has suggested that online reviews websites can provide a customized review sorting system for each individual consumer. Consequently, drawing upon five consumer segments as well as 10 restaurant characteristics found in the literature, we propose a content-filtering recommender system that evaluates individual online reviews and assigns a numeric score to each review for each of the five consumer segments. The numeric scores can later be used to sort online reviews for individual consumers according to their taste for restaurants