Traditionally, we use networks to securely and efficiently convey specific information messages to one or more receivers. However, communication networks today are increasingly used to serve a fundamentally different traffic, that delivers types of content rather than specific messages. For instance, when we want to find photos of an event, we may not care which specific photos we receive - we only care about the type of the content, namely, they are photos of the correct event. Content-type traffic pervades a host of applications today, such as search engines, recommender systems, and advertising networks.

Research on content-type networks is very popular, and most of the work looks at how to classify content into types, what to replicate, where and  how to store and from where to retrieve specific data. In our work, we investigate a totally different question: are there benefits in designing information transmission schemes specifically tailored to content-type traffic?

Our research indicates that in some cases, these benefits can be significant. We have explored this question in the framework of pliable index coding. We have shown that with pliable index coding we can realize exponential gains over conventional index coding using polynomial time algorithms. Moreover, we explored two application areas: recommender systems and distributed computing. In recommender systems, we ask how much we can gain in terms of bandwidth and user satisfaction, if recommender systems became bandwidth aware and took into account not only the user preferences, but also the fact that they may need to serve these users under bandwidth constraints, as is the case over wireless networks. In this setup, the user is satisfied to receive any message she does not already have, with a satisfaction proportional to her preference for that message. We have shown, through a number of scenaria we sample, that although the optimization problems are in general NP-hard, polynomial time algorithms with constant approximation ratio can be designed to achieve more than 80% of the benefits and to save 90% of bandwidth. In distributed computing, to improve the communication efficiency in the data shuffling phase, we have examined the pliable index coding problem under data shuffling constraints and designed a hierarchical data shuffling scheme that uses pliable index coding as a component. By leveraging the many possible shuffling choices to reduce the number of transmissions, our proposed shuffling scheme is able to achieve benefits up to O(ns/m) over index coding, where ns/m is the average number of workers caching a message, and m, n, and s are the numbers of messages, workers, and cache size, respectively.