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Content related to "content-based-recommendation"

(1) content-based-recommendation Recommender System for the Multi-Channel TV Production
This paper presents the concept of content recommendations for the production of multi-channel TV shows. Within the IST FP6 project "LIVE – Live Staging of Media Events" we are developing a production support system which will have a functionality of content recommendations and will support production of multi-channels programs. The paper outlines a concept of a recommender system for the multi-channel TV production and presents basic architecture and workflows within the system. The recommendation of the archive content for a given channel is personalized by taking into account the profile of the target audience.
(1) content-based-recommendation The evaluation of a hybrid recommender system for recommendation of movies
In this paper we present our approach to generation of movie recommendations. The idea of our hybrid approach is to first separately generate predicted ratings for movies using the contentbased and collaborative recommender modules. Predicted ratings from both recommender engines are then combined into final classification by the hybrid recommender using weighted voting scheme. The basis for the calculations are Pearson’s correlation coefficient, True Bayesian prediction and M5Rules decision rules. The evaluation of the system performance was based on the EachMovie data corpus, for around 7000 users. Preliminary results show that this approach works really well, while there is still some room for improvement.
(1) content-based-recommendation Personalised content search
An overview of different personalization approaches (content-based, collaborative.based, feedback gathering mechanisms, existing standards). How can this approaches be used in LIVE environment.
(1) content-based-recommendation First Specification of the Personalized Content Recommender System (Deliverable 6.1)
This document describes the first specification of the Recommender System, which is one of the five basic system components of the LIVE system. The specification is the result of the work carried out within the WP6, Personalization and Feedback. The specification is based on the user requirements described in the Deliverable 9.1, and the basic system architecture, de-fined in D9.2. The document provides a general view of the Recommender System which should allow for different usage scenarios. It represents the WP6’s current view of the final, fully functional Recommender System to be used in the production of LIVE TV programs. The document includes: • User requirements. Users of the Recommender System services and the functionalities that they require are defined. • Usage scenarios are proposed both for Video Conductor as well as for the Consumer. • Functionalities. A list of functionalities provided by the Recommender system is de-fined. • Based on the scenarios, six use cases of RS are defined in more detail. • Internal system architecture of the RS is defined including components that comprise the Recommender System. • Services and Interfaces. Services which are offered by the RS are described together with proposed interfaces. • Activity diagrams. The activity diagrams define workflows that are performed inside the recommender system.
(1) content-based-recommendation Predicting future User Behaviour in interactive live TV (EuroITV 2008 Paper)
Recommender systems are a means of personalisation providing their users with personalised recommendations of items that would possibly suit the users needs. They are used in a broad area of contexts where items are somehow linked to users. The creation of recommendations of interactive live TV suffers from several inherent problems, e.g. the impossibility to foresee the contents of the next items or the reactions of the user to the changing programme. This paper proposes an algorithm for building personalised streams within interactive live TV. The development of the algorithm comprises a basic model for users and media items. A first preliminary evaluation of the alogithm is executed and the results discussed. In M. Tscheligi, M.Obrsit, and A. Lugmayr (Eds.): EuroITV 2008, LNCS 4066, pp.117-121, 2008, Springer-Verlag, Berlin