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Interest-based recommendations for business intelligence users

机译：针对商业智能用户的基于兴趣的建议

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摘要

It is quite common these days for experts, casual analysts, executives and data enthusiasts, to analyze large datasets through user-friendly interfaces on top of Business Intelligence (BI) systems. However, current BI systems do not adequately detect and characterize user interests, which may lead to tedious and unproductive interactions. In this paper, we propose a collaborative recommender system for BI interactions, specifically designed to take advantage of identified user interests. Such user interests are discovered by characterizing the intent of the interaction with the BI system. Building on user modeling for proactive search systems, we identify a set of features for an adequate description of intents, and a similarity measure for grouping intents into coherent clusters. On top of these automatically identified interests, we build a collaborative recommender system based on a Markov model that represents the probability for a user to switch from one interest to another. We validate our approach experimentally with an in-depth user study, where we analyze traces of BI navigation. Our results are two-fold. First, we show that our similarity measure outperforms a state-of-the-art query similarity measure and yields a very good precision with respect to expressed user interests. Second, we compare our recommender system to two state-of-the-art systems to demonstrate the benefit of relying on user interests. (C) 2018 Elsevier Ltd. All rights reserved.
机译：如今，专家，临时分析师，高管和数据爱好者通过商务智能（BI）系统之上的用户友好界面分析大型数据集已经很普遍。但是，当前的BI系统无法充分检测和表征用户兴趣，这可能导致乏味且无用的交互。在本文中，我们为BI交互提出了一个协作式推荐系统，该系统专门设计用于利用已识别的用户兴趣。通过描述与BI系统交互的意图来发现这种用户兴趣。在针对主动搜索系统的用户建模的基础上，我们确定了一组功能，以充分描述意图，并采用相似性度量将意图分组为连贯的集群。在这些自动识别的兴趣之上，我们基于马尔可夫模型构建了一个协作推荐系统，该系统代表用户从一种兴趣转换为另一种兴趣的可能性。我们通过深入的用户研究实验性地验证了我们的方法，在该研究中我们分析了BI导航的痕迹。我们的结果有两个方面。首先，我们证明了我们的相似性度量优于最新的查询相似性度量，并且在表达的用户兴趣方面产生了非常好的精度。其次，我们将推荐系统与两个最先进的系统进行比较，以证明依靠用户兴趣的好处。 （C）2018 Elsevier Ltd.保留所有权利。

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