Cosimo Palmisano

Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems

Recently, methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. Although some of these methods have been studied independently, no prior research compared the performance of these methods to determine which of them is better than the others. This paper focuses on comparing the pre-filtering and the post-filtering approaches and identifying which method dominates the other and under which circumstances. Since there are no clear winners in this comparison, we propose an alternative more effective method of selecting the winners in the pre- vs. the post-filtering comparison. This strategy provides analysts and companies with a practical suggestion on how to pick a good pre- or post-filtering approach in an effective manner to improve performance of a context-aware recommender system.

Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-Commerce

Recent literature predicts that including context in a recommender system may improve its performance. The context-based recommendation approaches are classified as pre-filtering, post-filtering and contextual modeling. Little research has been done on studying whether including context in a recommender system improves the recommendation performance and no research has compared yet the different approaches to contextual RS. The research contribution of this work lies in studying the effect of the context on the recommendation performance and comparing a pre-filtering approach to a post-filtering using a collaborative filtering recommender system.

Contextual Segmentation: Using Context to Improve Behavior Predictive Models in E-commerce

In e-commerce, where the search costs are low and the competition is just a mouse click away, it is crucial to accurately predict customer purchasing behavior in order to offer more targeted and personalized products and services. Recent research has demonstrated that including the context in which a transaction occurs in customer behavior models improves their predictive performance, especially when studying individual customer behavior. However, several practical and managerial issues can arise, thus driving companies to focus on segments rather than on individuals. The main contribution of this work lies in presenting a conceptual framework to incorporating context when building predictive models of market segments, and in comparing different approaches, across a wide range of experimental conditions. Our experiments show that the most accurate approach is not the most efficient from a managerial perspective. Our findings provide insights of how companies can exploit context at best to support marketing decision-making.

The Effect of Context on the Predictive Performance of Segmentation

Recent research showed that including context in customer behavior models improves predictive performance, especially when the unit of analysis is the single customer. Also segmentation has proved to improve the performance of predictive modeling. The research contribution of this work lies in studying interaction effects between segmentation and contextual information. Several experiments were done on a data set coming from an e-commerce application.

Using Contextual Information to Decrease the Cost of Incorrect Predictions in On-line Customer Behavior Modeling

The performance of user profiling models depends on both the predictive accuracy and the cost of incorrect predictions. In this paper we study whether including contextual information leads to a decrease in the misclassification cost. Several experimental analyses were done by varying the cost ratio, the market granularity and the granularity of context. The experimental results show that context leads to a decrease in the misclassification cost under particular conditions. These findings have significant implications for companies that have to decide whether to gather contextual information and make it actionable: how deep it should be and which unit of analysis to consider in market research.

Using Contextual Information in Transactional Segmentation: An Empirical Study in E-Commerce

The growing complexity and variability characterizing markets have induced scholars and marketers to propose new segmentation approaches. Recent research has shown that including the context in which a transaction occurs in customer behavior models, improves the ability of predicting their behavior. However, no systematic research has studied whether contextual information really matters in market segmentation. To this aim we conducted an empirical study in an e-commerce application across a wide range of experimental conditions. The results show that context strongly affects the composition of segments. Moreover, including context in the segmentation approach can improve both the homogeneity of segments and the ability of predicting customer behavior. Finally in some experimental conditions, the finer contextual information is, the better segmentation results are. Some managerial implications related to the benefits and complexity of a contextual segmentation are discussed.

Employee profiling in the total reward management

The Human Resource departments are now facing a new challenge: how to contribute in the definition of incentive plans and professional development? The participation of the line managers in answering this question is fundamental, since they are those who best know the single individuals; but they do not have the necessary background. In this paper, we present the Team Advisor project, which goal is to enable the line managers to be in charge of their own development plans by providing them with a personalized and contextualized set of information about their teams. Several experiments are reported, together with a discussion of the results.