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A Hybrid Recommender Strategy for Personalized Utility-Based Cross-Modal Multimedia Adaptation

Enabling transparent and augmented use of multimedia content across a wide range of networks and devices is still a challenging task within the multimedia research community. Within multimedia frameworks, content adaptation is the core concept to overcome this issue. Most media adaptation engines targeting Universal Multimedia Access (UMA) scale the content w.r.t. terminal capabilities and network resource constraints and do not sufficiently consider user preferences. This paper focuses on a hybrid recommender technique for configuring a cross-modal utility model that guides adaptation of multimedia content. This approach additionally considers the user environment as well as demographic user data which leads to a personalized and increased multimedia experience. Based on a related adaptation decision technique we show how it is possible to offer a personalized adaptation for the individual user. We present a detailed evaluation of the approach based on results earned by subjective tests.