ABSTRACTIn 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.

中文翻译：

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大型在线对话中迷失方向的推荐系统的适用性

摘要在基于注释的大型文献讨论的背景下，本研究检查了推荐系统对用户迷失方向的启示。从关于群体认知、知识构建和推荐系统的文献中汲取见解，我们开发了三个推荐系统，并在 136 位用户上测试了这些系统。结果表明，具有约束 Pearson 相关系数相似性度量的推荐系统减少了用户的迷失方向，并使他们有机会根据他们的需求和偏好更好地了解有趣和相关的信息，而无需花费大量时间和精力。关于其他软件条件，结果表明用户遭受更高程度的迷失方向。这些发现反驳了注释减少迷失方向的说法。