Analysis of Factors Influencing User Trust in Machine Learning-Based Information Recommendations

Trust in machine learning-based recommendations can be influenced by various factors. One such factor is information quality, which encompasses the relevance, accuracy, completeness, and timeliness of the provided information. System transparency also plays a role, as users often require explanations regarding the rationale behind specific information or recommendations. Another relevant factor is perceived usefulness the user's perception of how beneficial a recommendation is in meeting needs or completing a task. Perceived ease of use, user experience, and perceptions regarding data security and privacy also factor into the formation of trust in recommendation systems. This study employs the Systematic Literature Review (SLR) method to identify, examine, and analyze prior research findings regarding the factors influencing user trust in machine learning-based information recommendations. A review of various studies on machine learning based recommendation systems reveals that user trust in information recommendations is not formed solely by the technology's ability to generate accurate suggestions. Instead, user trust emerges from the interaction between the system's technical aspects, information quality, the transparency of the recommendation process, and the user's own experiences and characteristics.