Investigating the Effects of Different Levels of User Control on the Effectiveness of Context-Aware Recommender Systems for Web-Based Search

Recommender systems assist users by providing recommendations based on some filtration criteria to reduce information overload. Embedding context-awareness allows recommender systems to use context information around the user, situation, and system to adapt and provide more efficient, relevant, and personalized recommendations. However, embedding context-awareness into recommender systems inherently limits the users' control over the systems due to reduced interactivity from automatic adaptations. This may potentially impact users' use and perception of the systems. Control can be purposefully designed to be given to the user in context-aware recommender systems at different levels. Our work investigates the effects of different levels of user control on the effectiveness and understandability of context-aware recommender systems (CARS) within the scenario of learning through web-based search (called 'Search-As-Learning'). To enable our study, we implemented a CARS that supports web-based search by recommending users a link using context such as browsing history. Our study found that participants used more recommendations from the CARS with high control compared to no control and some control. In conclusion, higher control in a recommender system for web-based search is preferred by the user despite control manipulation taking more time possibly due to explicit user needs.