| dc.description.abstract | Recommendation systems are systems that seek for prediction and give users
recommendation about products or items that they might be interested in. There are two
common approaches, which have been proposed to perform recommendation system;
they are content-based filtering (CBF) and collaborative filtering (CF). CBF methods are
based on the description of previously preferred items to predict a target user’s rating. On
the other hand, CF methods are based on neighbors’ ratings to predict a target user’s
rating. In this work, we consider recommendation on the context of Social TV (STV).
The watchers/users may either share, comment, rate, or tag videos in which they are
interested in. Each video must be watched and rated by many users. For these
assumptions, we proposed a novel model-based collaborative filtering using a fuzzy
neural network to learn user’s social web behaviors to make video recommendation on
STV. We use Netflix data-set to evaluate the proposed method. The result shown that the
proposed approach is a significant effective method.
Keywords: ANFIS, Ontology, Smart TV, Video, Recommendation system, and Neural
network. | en_US |
