We have developed a recommender system to assist project managers at MITRE with identifying and learning about potential candidates for open project positions. Features were added to the user interface for the recommender as a result of a pilot study in which the recommender was used to suggest job candidates for open positions. In this paper we explain how the recommender is implemented, descr...

There is a need for modeling and reasoning on the subjective, incomplete, imprecise and vague nature of item features, user behavior and their relationships in machine learning algorithms. This paper presents results of analysis of effects of fuzzy theoretic inference/recommendation strategies and fuzzy theoretic similarity measures on a recommender system's accuracy-measured in terms of precis...

Personalized services in websites continue to increase with a distinct tendency toward the user-oriented paradigm; however, current personalized services related to website design are based on a very simple type of personalization, such as merely showing assembled webpages to a user according to his/her selections out of various design alternatives. Such a type of personalization has two proble...

World Wide Web is the biggest source of information. Though the World Wide Web contains a tremendous amount of data, most of the data is irrelevant and inaccurate from users‟ point of view. Consequently it has become increasingly necessary for users to utilize automated tools such as recommender systems in order to discover, extract, filter, and evaluate the desired information and resources. W...

We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science...

Knowledge-based recommender systems comprise one category of user-modeling system that can draw inferences from user models. This brief paper contains a global description of a multi-faceted, educational, knowledge-based recommender system, including a basic set of descriptors that the model contains, a taxonomy of inferences that might be made over such models, and a listing of literature that...

Recent work has focused on new measures that are beyond the accuracy of recommender systems. Serendipity, which is one of these measures, is defined as a measure that indicates how the recommender system can find unexpected and useful items for users. In this paper, we propose a Fusion-based Recommender System that aims to improve the serendipity of recommender systems. The system is based on t...

In our work with conversational recommender systems we have derived two dialogue strategies called interview and delivery. We explore the symmetry between preferential interview and traditional clarification questions, and arrive at basic interview and delivery strategies suitable for conversational recommender system implementations. The strategies are based on a corpus analysis of recommendat...

Nowadays Product advertisement and viewer’s choice are two important part of marketing. These two parts generate a system that system is known as Recommender system. Recommender system plays a vital role in internet technology for data gathering and rating up a data. There are four types of filtering technique used in Recommender System-demographic, content, collaborative and hybrid. The most w...

Recommender systems help users sort through vast quantities of information. Sometimes, however, users do not know if they can trust the recommendations they receive. Adding a confidence metric has the potential to improve user satisfaction and alter user behavior in a recommender system. We performed an experiment to measure the effects of a confidence display as a component of an existing coll...