A Novel Strategy for Recommending Multimedia Objects and its Application in the Cultural Heritage Domain

Abstract

One of the most important challenges in the information access field, especially for multimedia repositories, is information overload. To cope with this problem, in this paper, the authors present a strategy for a recommender system that computes customized recommendations for users’ accessing multimedia collections, using semantic contents and low-level features of multimedia objects, past behaviour of individual users, and social behaviour of the users’ community as a whole. The authors implement their strategy in a recommender prototype for browsing image digital libraries in the Cultural Heritage domain. They then investigate the effectiveness of the proposed approach, based on the users’ satisfaction. The preliminary experimental results show that the approach is promising and encourages further research in this direction.

A Fast SVC-Based Channel-Recommendation System for an IPTV on a Cloud and P2P Hybrid Platform

“…Albanese et al [20] proposed a novel approach to be recommended for multimedia objects, based on an 'importance ranking' algorithm that highly resembles the well-known PageRank ranking strategy.…”

“…To enable users to search for their favorite channels quickly, improving the architecture of IPTV does not suffice. We expect our platform to achieve the goals of user customization such as multimedia recommenders [19,20]. Therefore, we propose an efficient mechanism of channel recommendations by using user logs to train and establish the preview tree (P-tree) for each user.…”