Predicting usefulness of Yelp reviews with localized linear regression models
Ruhui Shen, Jialiang Shen, Yuhong Li, Haohan Wang · 2016
Many websites such as Yelp provide platform for users to write reviews about places they have visited. But not all reviews are equally useful. However, it generally takes from several weeks to months to receive feedback about “usefulness” of review from online community. So there is a need to automatically predict the “usefulness” of review. In this paper, we are trying to solve the specific question “How many ‘useful’ votes a Yelp review will receive?” by using bag-of-words, linguistic, geographical, statistical, popularity and other qualitative features extracted from user, business and review information provided by Yelp. We use state-of-the-art machine learning algorithms for regression to predict required numeric value of ‘usefulness’ of review. We further gained performance improvement by introducing a batch mode localized weighted regression model. This localized regression approach resulted into RMSLE of 0.47769, which is better than traditional methods.