Preference modeling has a crucial role in customer relationship management systems. Traditional approaches to preference modeling are based on decision and utility theory by explicitly querying users about the behavior of value function, or utility of every outcome with regard to each decision criterion. They are error-prone and labor intensive. To address these limitations, computer based impl...

The number of resources of information has increased significantly and Information Retrieval (IR) based on keyword in web has become very significant.XML has become the widely used format for sharing of information. As the number of resources of information has increased significantly and retrieval of correct data according to user preference may not be achieved efficiently. In order to improve...

Peference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized “from scratch” by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In th...

We describe the interaction of three aspects core to a personalized scheduling task. First, we develop a preference model designed to capture user preferences for the task of scheduling a meeting request between multiple people, and a methodology for preference elicitation to initially populate this model. Second, we explain a natural-language-based elicitation of the meeting request details an...

Prior works in designing caching policy do not distinguish content popularity with user preference. In this paper, we optimize caching policy for cache-enabled device-to-device (D2D) communications by exploiting individual user behavior in sending requests for contents. We first show the connection between content popularity and user preference. We then optimize the caching policy with the know...

A formal model of machine learning by considering user preference of attributes is proposed in this paper. The model seamlessly combines internal information and external information. This model can be extended to user preference of attribute sets. By using the user preference of attribute sets, user preferred reducts can be constructed.

One of the most challenging tasks in the development of recommender systems is the design of techniques that can infer the preferences of users through the observation of their actions. Those preferences are essential to obtain a satisfactory accuracy in the recommendations. Preference learning is especially difficult when attributes of different kinds (numeric or linguistic) intervene in the p...

Ridesharing system has been recognized as an efficient transport mode to solve the environment, energy, traffic congestion issues. In recent researches, user preference was revealed as an important factor to enhance the performance and reliability of ridesharing systems. With the development in ICT, internet-enabled devices and social network enable us to obtain real-time travel information and...