Paper 10: Mobile Application Development: Web vs. Native

Title: Mobile Application Development: Web vs. Native
Authors: Andre Charland, and Brian LeRoux
Link: http://dl.acm.org/citation.cfm?id=1966989.1968203

In their article, Charland and LeRoux examine the challenges between developing natively for mobile platforms vs. making webapps. Writing a single application across multiple different platforms requires extensive knowledge of multiple SDKs and programming language, while web based development is severely limited by the speed and uses of Javascript. The physical configuration of the hardware can also vary greatly between phones and platforms.

In the end, the authors concluded that the most likely victor between these two methodologies is... neither. Native applications are uniquely suited for applications such as gaming. Web applications, while not useful for high performance applications, are particularly useful for writing applications that run on numerous, even unknown platforms. This characteristic is particularly useful for corporate environments. While this article was written as recently as 2011, it's interesting to note that multiple operating systems it profiled are no longer used regularly for smartphones, including Symbian, Windows Mobile, and WebOS.

Paper 9: Netflix Recommendations: Beyond the 5 stars

Title: Netflix Recommendations: Beyond the 5 stars
Author: Xavier Amatriain and Justin Basilico
Link: http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html

In their blog post, the Netflix team described how important recommendations are to their business, and how they use them. In the opening section, the authors described the Netflix Prize, and the various improvements that had come out of it. By combining Singular Value Decomposition and Restricted Boltzmann Machines, the winner of the Netflix prize was able to lower Netflix's RMSE of 0.9525 to 0.88.

The rest of the article was dedicated to describing how and where Netflix integrates it's recommendations. As well as the more obvious high-level genrea recommendations, Netflix uses it's recommendations to come up with more specific genera subtopics such as "Imaginative Time Travel Movies from the 1980s". Finally, Netflix stressed that explaining why movies are recommended is key to building user trust.

Paper 8: Using multiple feature models to design applications for mobile phones

Title: Using multiple feature models to design applications for mobile phones

Author: Clément Quinton, Sébastien Mosser, Carlos Parra, Laurence Duchien

Link: http://dl.acm.org/citation.cfm?id=2019136.2019162

The authors of this paper described their mobile phone application framework, ApplIDE. ApplIDE allows an app developer to design an application once, and target multiple hardware and OS configurations. Using the Eclipse Modeling Framework, ApplIDE can take into account if a phone lacks key hardware such as a front facing camera or GPS, and assemble the relevant modules accordingly. ApplIDE is focused primarily around Android, but also has support for iOS.

The authors of this paper were overall happy with the results. In the future, they wish to add expanded OS support, as well as support for more fine grained decision rules. Overall I found the system incredibly interesting, especially given the fragmented nature of Google's Android platform. I'm interested in seeing ApplIDE's work continue, so that hopefully systems like this man become the norm in the future.