Abstract
Access to information has been made easier in different domains that range from multimedia content, books, music, news, etc. To deal with the huge amount of alternatives, recommendation systems have been often used as a solution to filter the options and provide suggestions of items that might be of interest to an user. The news domain introduces additional challenges due not only to the large amount of new items produced daily but also due to their ephemeral timelife. In this paper, a news recommendation system which combines content-based and georeferenced techniques in a mobility scenario, is proposed. Taking into account the volatility of the information, short-term and long-term user profiles are considered and implicitly built. Besides tracking users' clicks, the system infers different levels of interest an article has by tracking and weighting each action in the system and in social networks. Impact of the different fields that make up a news is also taken into account by following the inverted pyramid model that assumes different levels of importance to each paragraph of the article. The solution was tested with a population of volunteers and results indicate that the quality of the recommendation approach is acknowledged by the users.

Abstract
Efficient access to large scale video assets, may it be our life memories in our hard drive or a broadcaster archive which the company is eager to sell, requires content to be conveniently annotated. Manually annotating video content is, however, an intellectually expensive and time-consuming process. In this paper we argue that crowdsourcing, an approach that relies on a remote task force to perform activities that are costly or time-consuming using traditional methods, is a suitable alternative and we describe a solution based on gamification mechanisms for collaboratively collecting timed metadata. Tags introduced by registered players are validated based on a collaborative scoring mechanism that excludes erratic annotations. Voting mechanisms, enabling users to approve or refuse existing tags, provide an extra guarantee on the quality of the annotations. The sense of community is also created as users may watch the crowd's favourite moments of the video provided by a summarization functionality. The system was tested with a pool of volunteers in order to evaluate the quality of the contributions. The results suggest that crowdsourced annotation can describe objects, persons, places, etc. correctly, as well as be very accurate in time.

Abstract
The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.

Abstract
The expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs' characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

Abstract
Creating descriptive labels for videos is an important task, with application in video retrieval, Web accessibility and computer vision. However automatic creation of such labels is difficult and, alternatively, having professionals manually describing content is too expensive. Engaging end-users in the process of describing multimedia assets may lead to good results and enables creating the sense of participation which is currently one of the key factors to attract customers to a service. The existing approaches are highly successful in terms of number of engaged players and number of collected labels, but hardly create comprehensive tag sets, contributing both with generic or too narrow meaning tags. "Games With A Purpose" are one of the approaches that have been used in an attempt to create comprehensive video descriptions by harnessing the intelligence of human players and have them contributing and collaborating towards a common goal that is recognized if successful. This paper describes a game which implements two mechanisms for collecting data via human-based computation games. Tags introduced by registered players, in a given timecode, are validated based on a collaborative scoring mechanism that eliminates irregular annotations. Additionally, a voting mechanism that enables players to endorse or refuse existing tags, provides an extra instrument to guarantee the quality of the annotations. © 2013 ACM.

Abstract
In movie/TV collaborative recommendation approaches, ratings users gave to already visited content are often used as the only input to build profiles. However, users might have rated equally the same movie but due to different reasons: either because of its genre, the crew or the director. In such cases, this rating is insufficient to represent in detail users' preferences and it is wrong to conclude that they share similar tastes. The work presented in this paper tries to solve this ambiguity by exploiting hidden semantics in metadata elements. The influence of each of the standard description elements (actors, directors and genre) in representing user's preferences is analyzed. Simulations were conducted using Movielens and Netflix datasets and different evaluation metrics were considered. The results demonstrate that the implemented approach yields significant advantages both in terms of improving performance, as well as in dealing with common limitations of standard collaborative algorithm.