Despite the growing popularity of Context-Aware Recommender Systems (CARSs), only limited work has been done on how contextual recommendations affect the behavior of customers in real-life settings. In this paper, we study the effects of contextual recommendations on the purchasing behavior of customers and their trust in the provided recommendations. In particular, we did live controlled experiments with real customers of a major commercial Italian retailer in which we compared the customers' purchasing behavior and measured their trust in the provided recommendations across the contextual, content-based and random recommendations. As a part of this study, we have investigated the role of accuracy and diversity of recommendations on customers' behavior and their trust in the provided recommendations for the three types of RSes. We have demonstrated that the context-aware RS outperformed the other two RSes in terms of accuracy, trust and other economics-based performance metrics across most of our experimental settings.

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.

The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is "cleverly" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information.

The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications have been done before. In this paper, we address this problem. To this aim, we collected data containing rich contextual information by developing a special-purpose browser to help users to navigate a well- known e-commerce retail portal and purchase products on its site. The experimental results show that context does matter for the case of modeling behavior of individual customers. The granularity of contextual information also matters, and the effect of contextual information gets diluted during the process of aggregating customers' data.