Which Data Quality Model for Recommender Systems?

Although data quality has been acknowledged as a significant issue in a variety of information systems research studies, it has received little attention in recommender systems. Data quality is crucial for the performance and effectiveness of recommender systems. Recommender systems rely on historical data to make predictions and recommendations, and the accuracy of these recommendations heavily depends on the quality of the data used. However, the reliability of these recommendations are substantially impacted by the quality of the data used. To evaluate and enhance the performance of recommender systems, it is crucial to comprehend the dimension of data quality. This paper addresses the gap by conducting a comprehensive literature assessment on data quality dimensions and models in the context of recommender systems. It draws attention to the various dimension of data quality, looks at the data models and offers suggestion models for assessing and enhancing data quality. This paper lays the groundwork for future studies and advancements in data quality for recommender systems, which will ultimately result in recommendations for users that are more accurate and trustworthy.