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IntersectionExplorer, a multi-perspective approach for exploring recommendations

机译：交叉路口，一种探索建议的多视角方法

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摘要

In this paper, we advent a novel approach to foster exploration of recommendations: IntersectionExplorer, a scalable visualization that interleaves the output of several recommender engines with human-generated data, such as user bookmarks and tags, as a basis to increase exploration and thereby enhance the potential to find relevant items. We evaluated the viability of IntersectionExplorer in the context of conference paper recommendation, through three user studies performed in different settings to understand the usefulness of the tool for diverse audiences and scenarios. We analyzed several dimensions of user experience and other, more objective, measures of performance. Results indicate that users found IntersectionExplorer to be a relatively fast and effortless tool to navigate through conference papers. Objective measures of performance linked to interaction showed that users were not only interested in exploring combinations of machine-produced recommendations with bookmarks of users and tags, but also that this "augmentation" actually resulted in increased likelihood of finding relevant papers in explorations. Overall, the findings suggest the viability of IntersectionExplorer as an effective tool, and indicate that its multi-perspective approach to exploring recommendations has great promise as a way of addressing the complex human-recommender system interaction problem.
机译：在本文中，我们对提出建议的探索进行了一种新的方法：itEssexplorer，可扩展的可视化，可与人类生成的数据（例如用户书签和标签）交错多个推荐器的输出，作为增加探索的基础，从而提高有可能找到相关项目。我们在会议论文建议的背景下评估了InterSespexplorer的可行性，通过不同的设置，以了解不同的观众和场景工具的有用性。我们分析了用户体验的几个维度和其他，更客观，性能衡量标准。结果表明，用户发现IntersectionExplorer是一种相对快速而轻松的工具，可以通过会议论文导航。与互动相关的绩效目标措施表明，用户不仅对探索机器制作的建议的组合与用户和标签的书签感兴趣，而且这也是如此“增强”实际上导致在探索中寻找相关论文的可能性增加。总的来说，调查结果表明交叉攻击者作为一个有效工具的可行性，并表明其多视角探索建议的方法具有很大的希望，作为解决复杂的人推荐系统互动问题的方式。

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