Umberto Panniello

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 context-aware recommender systems in terms of accuracy and diversity

Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.

Incorporating context into recommender systems: an empirical comparison of context-based approaches

Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identifying which method dominates the others and under which circumstances. Although some of these methods have been studied independently, no prior research compared the relative performance to determine which of them is better. This paper proposes an effective method of comparing the three methods to incorporate context and selecting the best alternatives. As a result, it provides analysts with a practical suggestion on how to pick a good approach in an effective manner to improve the performance of a context-aware recommender system.

Are you providing the “right” customer experience? The case of Banca Popolare di Bari

Purpose – The purpose of this paper is to model customer experience (CE) as a “continuum”, labelled customer experience continuum (CEC). The paper adopts a CE quality construct and scale (EXQ) to determine the effect of CE on a banks marketing outcomes. The paper discusses the studys theoretical and managerial implications, focusing on CE strategy design. Design/methodology/approach – The paper empirically test a scale to measure customer experience quality (EXQ) for a retail bank. The paper interviews customers using a means-end-chain approach and soft-laddering to explore their CE perceptions with the bank. The paper classifies their perceptions into the categories of “brand experience” (pre-purchase), “service experience” (during purchase), and “post-purchase experience”. After a confirmatory factor analysis, the paper conducts a survey on a representative customer sample. The paper analyses the survey results with a statistical model based on the partial least squares method. The paper tests three h...

Unveiling the breakthrough potential of established technologies: an empirical investigation in the aerospace industry

ABSTRACT The present article sheds new light on the role of established technologies as a driving force behind technological evolution, hence unveiling their breakthrough potential. Specifically, going against the conventional wisdom that only nascent technologies significantly shape future technological developments, we examine the likelihood that established technologies have to become breakthrough solutions. Furthermore, we also analyse if and how the breadth of knowledge base characterising those inventions influences this probability. Based on a sample of 21,000 patents belonging to the aerospace industry granted at the United States Patent and Trademark Office (USPTO), our results reveal that established technologies have an inverted U-shaped effect on the likelihood of becoming breakthroughs, and that such relationship is negatively influenced by a wide knowledge breadth.

Research Note—In CARSs We Trust: How Context-Aware Recommendations Affect Customers’ Trust and Other Business Performance Measures of Recommender Systems

Most of the work on context-aware recommender systems has focused on demonstrating that the contextual information leads to more accurate recommendations. Little work has been done, however, on studying how much the contextual information affects the business performance. In this paper, we study how including context in recommendations affects customers’ trust, sales, and other crucial business-related performance measures. To do this, we delivered content-based and context-aware recommendations through a live controlled experiment with real customers of a commercial European online publisher. We measured the recommendations’ accuracy and diversification, how much customers spent purchasing products during the experiment, the quantity and price of their purchases, and the customers’ level of trust. We show that collecting and using contextual information in recommendations affects business-related performance measures, such as company sales, by improving the accuracy and diversification of recommendations, which in turn improves trust and, ultimately, business performance results.

Including Context in a Transactional Recommender System Using a Pre-filtering Approach: Two Real E-commerce Applications

Recent research has shown 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. Moreover, in real e-commerce applications, collecting ratings may be quite difficult. It is possible to use purchasing frequencies instead of ratings, but little research has been done. The research contribution of this work lies in studying when and how including context with a pre-filtering approach improves the performance of a recommender system using transactional data. To this aim, we studied the interaction between homogeneity and sparsity, in several experimental settings. The experiments were done on two databases coming from two actual e-commerce applications.

The effect of context on misclassification costs in e-commerce applications

Abstract The performance of customer behavior models depends on both the predictive accuracy and the cost of incorrect predictions. Previous research showed that including context in the customer behavior models can improve the accuracy. However, improving accuracy does not necessarily mean that the misclassification cost decreases. In fact, different errors have different costs. Even if the number of incorrect predictions decreases, the number of errors associated with higher costs increase. The aim of this paper is to understand whether including context in a predictive model reduces the misclassification costs and in which conditions this happens. Experimental analyses were done by varying the market granularity, the dependent variable and the context granularity. The results show that context leads to a decrease in the misclassification cost when the unit of analysis is the single customer or the micro-segment. The exceptions may occur when the unit of analysis is a segment. These findings have significant implications for companies that have to decide whether to gather context and how to exploit it best when they build predictive models of the behavior of their customers.

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.

Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a Retail Bank by a Recommender System Approach

Customer churn may be a critical issue for banks. The extant literature on statistical and machine learning for customer churn focuses on the problem of correctly predicting that a customer is about to switch bank, while very rarely consid-ers the problem of generating personalized actions to improve the customer retention rate. However, these decisions are at least as critical as the correct identification of customers at risk. The decision of what actions to deliver to what customers is normally left to managers who can only rely upon their knowledge. By looking at the scientific literature on CRM and personalization, this research proposes a number of models which can be used to generate marketing ac-tions, and shows how to integrate them into a model embracing both the analytical prediction of customer churn and the generation of retention actions. The benefits and risks associated with each approach are discussed. The paper also describes a case of application of a predictive model of customer churn in a retail bank where the analysts have also generated a set of personalized actions to retain customers by using one of the approaches presented in the paper, namely by adapting a recommender system approach to the retention problem.