CAPRE: A New Methodology for Product Recommendation Based on Customer Actionability and Profitability

International audienceRecommender systems can apply knowledge discovery techniques to the problem of making product recommendations. This aims to establish a customer loyalty strategy and thus to optimize the customer life time value. In this paper we propose CAPRE, a data-mining based methodology for recommender systems based on the analysis of turnover for customers of specific products. Contrary to classical recommender systems, CAPRE does not aspire to predict a customer's behavior but to influence that behavior. By measuring the actionability and profitability of customers, we have the ability to focus on customers that can afford to spend larger sums of money in the target business. CAPRE aggregates rules to extract characteristic purchasing behaviors, and then analyzes the counter-examples to detect the most actionable and profitable customers. We measure the effectiveness of CAPRE by performing a cross-validation on the MovieLens benchmark. The methodology is applied to over 10,000 individual customers and 100,000 products for the customer relationship management of VM Matériaux company, thus assisting the salespersons' objective to increase the customer value

Semantics-based classification of rule interestingness measures

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index

Interactive visual exploration of association rules with rule-focusing methodology

International audienceOn account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the user's focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm

Investigating differences in vigilance tactic use within and between the sexes in eastern grey kangaroos

Peer reviewedPostprin

Une méthodologie de recommandations produits fondée sur l'actionnabilité et l'intérêt économique des clients

National audienceDans un contexte économique difficile, la fidélisation des clients figure au premier rang des préoccupations des entreprises. En effet, selon le Gartner, fidéliser des clients existants coûterait beaucoup moins cher que prospecter de nouveaux clients. Pour y parvenir, les entreprises optimisent la marge et le cycle de vie des clients en développant une relation personnalisée aboutissant à demeilleures recommandations. Dans cet article, nous proposons une méthodologie pour les systèmes de recommandations fondée sur l'analyse des chiffres d'affaires des clients sur des familles de produits. Plus précisément, la méthodologie consiste à extraire des comportements de référence sous la forme de règles d'association et à en évaluer l'intérêt économique et l'actionnabilité. Les recommandations sont réalisées en ciblant les contre-exemples les plus actionnables sur les règles les plus rentables.Notreméthodologie est appliquée sur 12 000 clients et 100 000 produits de VMMatériaux afin d'orienter les commerciaux sur les possibilités d'accroissement de la valeur client

An Overview of Interaction Techniques and 3D Representations for Data Mining

International audienceAn Overview of Interaction Techniques and 3D Representations for Data Minin