The collaborative filtering recommendation based on sentiment analysis of online reviews
Wang We · Systems Engineering - Theory & Practice · 2014
Collaborative filtering recommendation algorithm bases on user behavior with similar interests to produce personalized recommendation.The core of the algorithm is to define the distance between the user's interest similarities.The paper considers the online review sentiment impact on user similarity recognition.In mixed products recommendation,coarse-grained sentimental polarity is identified;while in same category products recommendation,fine-grained sentimental analysis is employed for each feature.If the user's evaluation frequency is greater than the average on a special feature,it indicates that the user pays close attention to the feature;while if the user's rating is smaller than the average rating on a special feature,it means the user has a strict requirement on this feature.And then the user's preference model is created according to reviews,the higher the similarity between users in the reviews,the more consistent preferences between users.Experiment results show that the proposed collaborative filtering algorithm based on sentiment analysis of online reviews improves the traditional algorithm significantly on accuracy and recall.