We look at one of the informational novelties introduced by the existence of internet market, namely "the recommender systems". A recommender system is a system employed by some internet sellers, which collects data from all previous customers about their experiences and makes inferences from this data to recommend a product to an active customer. The recommender system can also be interpreted ...

CiteSeer is a digital library for scientific publications written by Computer Science researchers. Users are able to retrieve relevant documents from the database by searching by author name and/or keyword queries. Users may also receive recommendations of papers they might want to read provided by an existing conceptual recommender system. This system recommends documents based on an automatic...

This paper presents a description of integration of the Mr. DLib scientific recommender system into the JabRef reference manager. Scientific recommender systems help users identify relevant papers out of vast amounts of existing literature. They are particularly useful when used in combination with reference managers. Over 85% of JabRef users stated that they would appreciate the integration of...

The use of relevant metrics of software systems could improve various software engineering tasks, but identifying relationships among metrics is not simple and can be very time consuming. Recommender systems can help with this decision-making process; many applications have utilized these systems to improve the performance of their applications. To investigate the potential benefits of recommen...

The recommender system research community has become a well-identified and active research community built around the goal of understanding, building, and improving recommender systems. A recurring theme since the first workshop in Berkeley (March 1996) has been the development of substantial research infrastructure—typically publicly-accessible recommendation services—as a tool to conduct rese...

Along with the growth of the Internet, automatic recommender systems have become popular. Due to being intuitive and useful, factorization based models, including the Nonnegative Matrix Factorization (NMF) model, are one of the most common approachs for building recommender systems. In this study, we focus on how a recommender system can be built for online services and how the parameters of an...

In this paper we present the results of three investigations of our broad research on the usage of affect and personality in recommender systems. We improved the accuracy of a content-based recommender system with the inclusion of affective parameters in user and item modeling. We improved the accuracy of a content filtering recommender system under the cold start conditions with the introducti...