In interactive visual data exploration, users rely on recommendations on what data to explore next. EVLIN is a system that recommends queries to retrieve these data for the next exploration step, paired with suited visualizations. This paper extends EVLIN by combining its content-based recommendations with recommendations leveraging collaborative filtering to improve the effectiveness of recommendation-based visual data exploration. The recommendations rely on evolution provenance, which tracks users’ interactions during interactive visual data exploration. As more users explore a dataset, the evolution provenance of individual user explorations is incrementally integrated into a multi-user graph, for which we present match and merge algorithms. To compute collaborative-filtering recommendations, we present a search algorithm and optimizations to efficiently search queries similar to a current user’s query in the multi-user graph and give preference to queries that have been previously explored in an exploration step succeeding those similar queries. Our experimental evaluation studies the efficiency and effectiveness of the solutions proposed in this paper and demonstrates that using the full system with both content-based and collaborative-filtering recommendations enabled allows for effective interactive visual data exploration.

在交互式可视数据浏览中，用户依赖于下一步要浏览的数据的建议。EVLIN是一个系统，它建议查询以检索下一个探索步骤的这些数据，并提供合适的可视化效果。本文通过将基于内容的推荐与利用协作过滤的推荐相结合来扩展EVLIN，以提高基于推荐的视觉数据浏览的有效性。这些建议依赖于进化出处，该出处在交互式视觉数据探索期间跟踪用户的交互。随着越来越多的用户浏览数据集，单个用户浏览的演化源逐渐集成到多用户图中，为此我们提出了匹配和合并算法。要计算协作过滤建议，我们提出了一种搜索算法和优化方法，可以有效地在多用户图中搜索类似于当前用户查询的查询，并优先考虑先前在探索步骤中继这些相似查询之后已经进行查询的查询。我们的实验评估研究了本文提出的解决方案的效率和有效性，并证明了使用同时启用基于内容和协作筛选建议的完整系统可以进行有效的交互式可视数据浏览。