User Interests Clustering in Business Intelligence Interactions

Abstract

Abstract. It is quite common these days for experts, casual analysts, executives or data enthusiasts, to analyze large datasets using userfriendly 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 to identify such user interests by characterizing the intent of the interaction with the BI system. With an eye 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 interests. We validate experimentally our approach with a user study, where we analyze traces of BI navigation. 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.

Customer Segmentation through Path Reconstruction

“…Consequently, clustering is useful in that it can lead to the discovery of previously unknown groups within the data. Cluster analysis has been widely used in many applications such as Image Pattern Recognition [ 26 , 27 ], Business Intelligence [ 28 ], or Information Retrieval [ 29 , 30 ].…”

A Subjective Interestingness measure for Business Intelligence explorations

“…Both approach seems quite complementary as De Bie's approach makes possible query recommendation through interesting data discovery and Wanyu's approach instead allows similar recommendation through intent discovery. Interestingly, these intents are probability distribution over elements of knowledge, while other works have focused on capturing long or short term intents related to topics for data exploration such as [13].…”

“…Business intelligence (BI) exploration can be seen as an iterative process that involves expressing and executing queries over multidimensional data (or cubes) and analyzing their results, to ask more focused queries to reach a state of knowledge that allows to answer a business question at hand. This complex task can become tedious, and for this reason, several approaches have been proposed to facilitate the exploration by pre-fetching data [26], detecting interesting navigation paths [28], recommending appropriate queries based on past interactions [1] or by modeling user intents [13].…”