… member activity in the MovieLens movie recommendation system… the history of MovieLens
and the MovieLens datasets. We … limitations of using the MovieLens datasets in new research. …

… ever MovieLens … MovieLens to test our design for a wireless interface. Since then we have
had 180 people use MLU on their PDAs. We set up the field trial as a service to MovieLens …

… was to try new MovieLens features. Under … MovieLens and another to help maintain the
MovieLens database, again randomly ordered. Below these links was the link to the MovieLens …

… Therefore, the method presented in this research is implemented in two steps on the MovieLens
datasets. First, different classes are created using group classification and the ensemble …

Most of recommender systems have serious difficulties on providing relevant services to the
“short-head” users who have shown intermixed preferential patterns. In this paper, we …

Decisions are taken by humans very often during professional as well as leisure activities. It
is particularly evident during surfing the Internet: selecting web sites to explore, choosing …

… Next, we present the requirements that drove the design for MovieLens Unplugged. In
Section 4 we present our high level architecture and design for the system, and provide a …

… Since the original Movielens and Netflix datasets are large, … letters and items (movies in
Movielens or Netflix and stories in … In Movielens and Netflix, we have more complete information …

Prior work relevant to incorporating personality into recommender systems falls into two
categories: social science studies and algorithmic ones. Social science studies of preference …

The primary objective of the present work is two-fold. First, to compare the different filtering
techniques namely BayesNet, Decision Table, Logistic, k-NN, JRip, LibSVM, Randomized …