An efficient customer behavior analysis is important for good Recommender System. Customer transaction clustering is usually the first step towards the analysis of customer behavior. Traditionally data mining techniques are deployed in order to provide effective recommendation based on large population of customer transactions in real time. Customer transactions are likely to be imprecise and i...

In the last two years, mainly practitioners published newspapers and technical reports outlining the benefits and obstacles of Cloud Computing. Scientific research is limited to technical issues of Cloud Computing so far. Marketing and economic issues have been barely discussed in literature. Especially, customer considerations and pricing are only discussed vaguely in industry reports. A detai...

Customers are commonly not able to provide preferences that are technical enough to be used in the internal algorithms of knowledge-based recommender systems. In this paper, we present an approach to use a Bayesian network to infer technical preferences from customer answers obtained through a conversational elicitation process. The inferred preferences can be used in conjunction with a variety...

In an era of global customization, dominating the majority market with a single product has become increasingly difficult and almost impossible for most companies. In contrast, they must provide various product varieties that attract diverse customers, particularly when acquiring distinct market segments. In practice, however, most companies cannot effectively reduce the gap between customer re...

Advanced personalized e-applications require comprehensive knowledge about their user’s likes and dislikes in order to provide individual product recommendations, personal customer advice and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. ...

• Suppose we are attempting to model the buying preferences of several consumers based on past purchases, e.g. as in the Netflix recommender system. We assume that people with similar tastes tend to buy similar items and their buying history is related. Inferring the preferences for a customer based only on his past purchases may be tough, because that customer may not have rated enough movies ...

Effective recommendation is indispensable to customized or personalized services. Collaborative filtering approach is a salient technique to support automated recommendations, which relies on the profiles of customers to make recommendations to a target customer based on the neighbors with similar preferences. However, traditional collaborative recommendation techniques only use static informat...