Integrating Item Based Stereotypes in Recommender Systems

With the growing popularity of e-commerce, recommender systems play a critical role to enhance the user experience and increase sales revenue and profitability for a company. However, the accuracy of recommender systems is often suffering from data sparsity and the new user/item problems. A promising approach in solving the new user problem is stereotype based modeling. The proposed PhD research will go one step further and develop an item based recommender system employing the stereotype approach for item modeling. For the evaluation of our stereotype-based recommender system, the study will employ the MovieLens and IMDb datasets. These two datasets are integrated using the iSynchronizer tool that was developed by the researchers for tasks such as this. Early evaluation results demonstrate promising prediction accuracy with user-based stereotypes, especially for users with a small number of existing ratings.