Netflix : a house of cards or the new HBO?

'House of Cards' was a $100 million, 13-episode TV series starring Kevin Spacey and directed by David Fincher, that premiered in 2013 exclusively online, available only to Netflix subscribers. For over a year, Netflix had received much media attention for both its rapid international expansion and for its financial woes resulting in a plummeting share price and had some financial analysts postulating that it might not even survive. This article asks how can a company that teetering on the brink spend $100 million commissioning a TV series, let alone push ahead with global expansion? It asks is Netflix’s business model sustainable? And if it is, what does its entry into original programming mean for the future of television?

What do Netflix, Stan and Presto mean for Australian TV?

The arrival of subscription video on demand services Netflix, Stan and Presto have implications for what we call "television" in Australia – and much of the policy detail remains to be hammered out.

Regulation on ‘cross-border portability’ of online content services: Roaming for Netflix or the end of copyright territoriality? CEPS Commentary, 15 February 2016

In a communication to the Parliament and the Council entitled “Towards a modern, more European copyright framework” and dated 9 December 2015,1 the European Commission confirmed its intention to progressively remove the main obstacles to the functioning of the Digital Single Market for copyrighted works. The first step of this long-term plan, which was first announced in Juncker’s Political Guidelines2 and the Communication on “A Digital Single Market strategy for Europe”,3 is a proposal for a regulation aimed at ensuring the so-called ‘cross-border portability’ of online services giving access to content such as music, games, films and sporting events.4 In a nutshell, the proposed regulation seeks to enable consumers with legal access to such online content services in their country of residence to use the same services also when they are in another member state for a limited period of time. On the one hand, this legislative proposal has the full potential to resolve the (limited) issue of portability, which stems from the national dimension of copyright and the persisting territorial licensing and distribution of copyright content.5 On the other hand, as this commentary shows, the ambiguity of certain important provisions in the proposed regulation might affect its scope and effectiveness and contribute to the erosion of the principle of copyright territoriality.

Media Economics

A concise introduction to the key ideas and issues in the study of media economics, drawing on a broad range of case studies - from Amazon and Twitter, to Apple and Netflix - to illustrate how economic paradigms are not just theories, but provide important practical insights into how the media operates today. Understanding the economic paradigms at work in media industries and markets is vitally important for the analysis of the media system as a whole. The changing dynamics of media production, distribution and consumption are stretching the capacity of established economic paradigms. In addition to succinct accounts of neo-classical and critical political economics, the text offers fresh perspectives for understanding media drawn from two 'heterodox' approaches: institutional economics and evolutionary economics. Applying these paradigms to vital topics and case studies, Media Economics stresses the value – and limits – of contending economic approaches in understanding how the media operates today. It is essential reading for all students of Media and Communication Studies, and also those from Economics, Policy Studies, Business Studies and Marketing backgrounds who are studying the media. Table of Contents: 1. Media Economics: The Mainstream Approach 2. Critical Political Economy of the Media 3. Institutional Economics 4. Evolutionary Economics 5. Case Studies and Conclusions

Screen Distribution and the New King Kongs of the Online World

Like music and the news media before it, the film and television business is now facing its time of digital disruption. Major changes are being brought about in global online distribution of film and television by new players, such as Google/YouTube, Apple, Amazon, Yahoo!, Facebook, Netflix and Hulu, some of whom massively outrank in size and growth the companies that run film and television today. Content, Hollywood has always asserted, is King. But the power and profitability in screen industries have always resided in distribution. Incumbents in the screen industries tried to control the emerging dynamics of online distribution, but failed. The new, born digital, globally focused, players are developing TV network-like strategies, including commissioning content that has widened the net of what counts as television. Content may be King, but these new players may become the King Kongs of the online world.

Is Foxtel most at risk in the new Game of Screens?

If the experience in other major television markets like the United States and Canada is anything to go by, the omens are mixed for Foxtel.

Why a movie tax will make even more of us pirates

In a nation of rampant illegal downloaders, a tax on movies and television downloads is the last thing we need. Australian consumers and content producers are among those likely to be worse off should Joe Hockey succeed in his efforts to extend GST to online video-on-demand services like Netflix. It is easy to see why Mr Hockey and his state treasurer counterparts have reportedly agreed to this move. That doesn’t mean it’s a good idea.

Distribution Revolution: Conversations about the Digital Future of Film and Television

Distribution Revolution is a collection of interviews with leading film and TV professionals concerning the many ways that digital delivery systems are transforming the entertainment business. These interviews provide lively insider accounts from studio executives, distribution professionals, and creative talent of the tumultuous transformation of film and TV in the digital era. The first section features interviews with top executives at major Hollywood studios, providing a window into the big-picture concerns of media conglomerates with respect to changing business models, revenue streams, and audience behaviors. The second focuses on innovative enterprises that are providing path-breaking models for new modes of content creation, curation, and distribution—creatively meshing the strategies and practices of Hollywood and Silicon Valley. And the final section offers insights from creative talent whose professional practices, compensation, and everyday working conditions have been transformed over the past ten years. Taken together, these interviews demonstrate that virtually every aspect of the film and television businesses is being affected by the digital distribution revolution, a revolution that has likely just begun. Interviewees include: • Gary Newman, Chairman, 20th Century Fox Television • Kelly Summers, Former Vice President, Global Business Development and New Media Strategy, Walt Disney Studios • Thomas Gewecke, Chief Digital Officer and Executive Vice President, Strategy and Business Development, Warner Bros. Entertainment • Ted Sarandos, Chief Content Officer, Netflix • Felicia D. Henderson, Writer-Producer, Soul Food, Gossip Girl • Dick Wolf, Executive Producer and Creator, Law & Order

Telstra on the TV casting couch to trump its telco peers

This week, Telstra announced it will shortly introduce a new streaming video set top box. For a number of reasons, this is a very smart move.

Infrastructure Disputes Shape Future of Streaming Media

In November 2010, tension between Internet infrastructure companies boiled over in a dispute between content distribution network (CDN) Level 3 and Internet service provider (ISP) Comcast. Level 3, a distribution partner of Netflix, accused Comcast of violating the principles of net neutrality when the ISP increased distribution fees for carrying high bandwidth services. Comcast justified its actions by stating that the price increase was standard practice and argued Level 3 was trying to avoid paying its fair share. The dispute exemplifies the growing concern over the rising costs of streaming media services. The companies facing these inflated infrastructure costs are CDNs (Level 3, Equinix, Limelight, Akamai, and Voxel), companies that host streaming media content on server farms and distribute the content to a variety of carriers, and ISPs (Comcast, Time Warner, Cox, and AT&T), the cable and phone companies that provide “last mile” service to paying customers. Both CDNs and ISPs are lobbying government regulators to keep their costs at a minimum. The outcome of these disputes will influence the cost, quality, and legal status of streaming media.

Optimal Data Distributions in Machine Learning

In the first part of the thesis we explore three fundamental questions that arise naturally when we conceive a machine learning scenario where the training and test distributions can differ. Contrary to conventional wisdom, we show that in fact mismatched training and test distribution can yield better out-of-sample performance. This optimal performance can be obtained by training with the dual distribution. This optimal training distribution depends on the test distribution set by the problem, but not on the target function that we want to learn. We show how to obtain this distribution in both discrete and continuous input spaces, as well as how to approximate it in a practical scenario. Benefits of using this distribution are exemplified in both synthetic and real data sets.

In order to apply the dual distribution in the supervised learning scenario where the training data set is fixed, it is necessary to use weights to make the sample appear as if it came from the dual distribution. We explore the negative effect that weighting a sample can have. The theoretical decomposition of the use of weights regarding its effect on the out-of-sample error is easy to understand but not actionable in practice, as the quantities involved cannot be computed. Hence, we propose the Targeted Weighting algorithm that determines if, for a given set of weights, the out-of-sample performance will improve or not in a practical setting. This is necessary as the setting assumes there are no labeled points distributed according to the test distribution, only unlabeled samples.

Finally, we propose a new class of matching algorithms that can be used to match the training set to a desired distribution, such as the dual distribution (or the test distribution). These algorithms can be applied to very large datasets, and we show how they lead to improved performance in a large real dataset such as the Netflix dataset. Their computational complexity is the main reason for their advantage over previous algorithms proposed in the covariate shift literature.

In the second part of the thesis we apply Machine Learning to the problem of behavior recognition. We develop a specific behavior classifier to study fly aggression, and we develop a system that allows analyzing behavior in videos of animals, with minimal supervision. The system, which we call CUBA (Caltech Unsupervised Behavior Analysis), allows detecting movemes, actions, and stories from time series describing the position of animals in videos. The method summarizes the data, as well as it provides biologists with a mathematical tool to test new hypotheses. Other benefits of CUBA include finding classifiers for specific behaviors without the need for annotation, as well as providing means to discriminate groups of animals, for example, according to their genetic line.

A Hierarchical Model for Ordinal Matrix Factorization

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based on Gibbs sampling and one based on variational Bayes. Importantly, these algorithms may be implemented in the factorization of very large matrices with missing entries. The model is evaluated on a collaborative filtering task, where users have rated a collection of movies and the system is asked to predict their ratings for other movies. The Netflix data set is used for evaluation, which consists of around 100 million ratings. Using root mean-squared error (RMSE) as an evaluation metric, results show that the suggested model outperforms alternative factorization techniques. Results also show how Gibbs sampling outperforms variational Bayes on this task, despite the large number of ratings and model parameters. Matlab implementations of the proposed algorithms are available from cogsys.imm.dtu.dk/ordinalmatrixfactorization.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

Alla scoperta di ItaSA: analisi di una community italiana di fansubbing

Questa tesi presenta un'analisi della community italiana di fansubbing di Italian Subs Addicted, nota come ItaSA. Dopo un'introduzione generale sul fenomeno del fansubbing, ovvero la traduzione amatoriale di sottotitoli, si passa a un'analisi della community stessa, della sua organizzazione interna e del test per diventare traduttori. Nel capitolo 4 vengono presentate le regole per la traduzione e la creazione di sottotitoli, con il supporto di screenshot di varie serie TV, e si ha la descrizione della traduzione di una puntata della serie TV "True Detective" in tempo reale. Il capitolo 5 tratta il rapporto tra gli utenti e lo staff della community, un elemento peculiare che contraddistingue il fansubbing dalla produzione di sottotitoli tradizionali. Il capitolo 6 analizza brevemente il controverso tema della legalità dei sottotitoli. Infine, nella conclusione ho inserito una questione che negli ultimi mesi è stata molto dibattuta nella community, ovvero le conseguenze dell’imminente avvento di Netflix, un servizio di TV online, sul futuro del fansubbing.

Analisi dei comportamenti degli utenti per lo sviluppo di un sistema di App Recommendation

Negli ultimi cinque anni lo sviluppo di applicazioni mobile ha visto un grandissimo incremento dovuto pricipalmente all’esplosione della diffusione di smartphone; questo fenomeno ha reso disponibile agli analisti una enorme quantità di dati sulle abitudini degli utenti. L’approccio centralizzato nella distribuzione delle applicazioni da parte dei grandi provider quali Apple, Google e Microsoft ha permesso a migliaia di sviluppatori di tutto il mondo di raggiungere con i loro prodotti gli utenti finali e diffondere l’utilizzo di applicativi installabili; le app infatti sono diventate in poco tempo fondamentali nella vita di tutti i giorni e in alcuni casi hanno sostituito funzioni primarie del telefono cellulare. Obiettivo principale di questo studio sarà inferire pattern comportamentali dall’analisi di una grossa mole di dati riguardanti l’utilizzo dello smartphone e delle app installabili da parte di un gruppo di utenti. Ipotizzando di avere a disposizione tutte le azioni che un determinato bacino di utenza effettua nella selezione delle applicazioni di loro interesse quando accedono al marketplace (luogo digitale da cui è possibile scaricare nuove applicazioni ed installarle) è possibile stimare, ovviamente con un certo margine di errore, dati sensibili dell’utente quali: Sesso, Età, Interessi e così via analizzandoli in relazione ad un modello costruito su dati di un campione di utenti ben noto. Costruiremo così un modello utilizzando dati di utenti ben noti di cui conosciamo i dettagli sensibili e poi, tramite avanzate tecniche di regressione e classificazione saremo in grado di definire se esiste o meno una correlazione tra le azioni effettuate su uno Smartphone e il profilo dell’utente. La seconda parte della tesi sarà incentrata sull'analisi di sistemi di raccomandazioni attualmente operativi e ci concentreremo sullo studio di possibili sviluppi sviluppi futuri di questi sistemi partendo dai risultati sperimentali ottenuti.