Affordances of Recommender Systems for Disorientation in Large Online Conversations

ABSTRACT

In the context of large annotation-based literature discussions, this research examines the affordances of recommender systems on users’ disorientation. Drawing insights from literature on group cognition, knowledge building, and recommender systems, we developed three recommender systems and tested these systems on 136 users. Results indicate that the recommender system with constrained Pearson correlation coefficient similarity metric reduced users’ disorientation and afforded them the opportunity to become better aware of interesting and relevant information based on their needs and preferences without heavy costs in terms of time and effort. With respect to other software conditions, results indicate that users suffered from higher levels of disorientation. These findings counter the claim that annotations reduce disorientation. Theoretical and practical implications are also discussed.

KEYWORDS:

• Annotation
• disorientation
• group Cognition
• heat Maps
• recommender systems