Emotions in recommender systems for discrepant-users

Recommender system (RS) predicts relevant items to a new user based on the purchase history of existing users. In this area, Collaborative Filtering (CF) is one of the popular approaches, uses the user rating data for recommendations. Generally, users express their opinion on the items purchased through user ratings. Considering the user rating data alone may not be sufficient to identify the user interests that are nearer to the user intent. So, there is a need to look into other data provided in the purchase history such as user reviews, user profile, and helpfulness. In this paper, we propose an approach Elaboration Likelihood Model for Discrepant-users (ELM-D) to address the issue of identifying user interest using user rating data and user review data. In this approach, we use elaboration to identify the correct data to reach the user interests nearer to the user intent for discrepant-users. We observed real-world datasets and found the discrepant-users. The discrepant-users are the users who do not provide user ratings and/or reviews correctly. To understand the user interests from user review data, we proposed an approach to extract the user emotions using emotion detection algorithm by exploiting n-polarity. We built a CF approach to predict the interest of a new user using user rating data and user emotions from user review data. We conducted experiments on the real-world datasets from Amazon and Yelp. We evaluated results using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) metrics. The proposed approach outperforms the existing approaches.