Amarajyothi Aramanda
ABSTRACT
In recommender systems, an item is recommended to a new user by analyzing purchase history of existing users along with the items’ information. In this area, collaborative filtering is a popular approach to predict user’s preferences from the existing users’ preferences. The user’s preferences are identified from the user rating and/or review data. It is observed in the real-world data that user is not expressing the same feelings in user rating and review text. As a result, the accuracy of the recommender system is affected. In this paper, we address this inconsistency and propose an approach called Emotion-Specific Prediction to refine the user ratings by applying proposed emotion detection algorithm on review text. The emotion detection algorithm extracts the user feelings as emotional features by exploiting the multi-polarity from review text. The proposed approach transforms these features into refined ratings and are used to predict the user ratings using collaborative filtering. The experimental evaluations conducted on real-world Amazon and Yelp data sets, and results show that the proposed approach reduces root mean square error (RMSE) and Normalized RMSE (NRMSE) significantly.
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Index Terms
- Refining User Ratings Using User Emotions for Recommender Systems
Recommendations
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