Online social networks have been charged with enhancing and augmenting polarization in society. This polarization can have negative repercussions on the health of both individuals and society on the whole. Hence, it is vital that our online social networks are powered by algorithms that avoid polarisation and seek to curtail it. One target would be the curated news feed supplied to users in online social networks.

One new trending feature of Smartphones is the support for E-SIM (Embedded Subscriber Identification Module) cards. These allow the user to simultaneously subscribe to multiple cellular providers while also supporting at most one physical SIM (Subscriber Identification Module) card. This feature allows customers to easily switch between providers and is especially useful for those who use prepaid plans which are popular in developing countries. A customer may have multiple providers and, at any point in time, can choose the provider with the most cost effective data plan. This means that cellular providers must now take into account “soft-churn”, where the consumer dynamically switches between multiple plans from multiple providers, in addition to the more traditional churn where a consumer switches providers. This means that data pricing for such consumers must now be more personalized in order to be competitive and maximize profits. We determine the optimal personalized prepaid plan for such users while providing a competitive advantage to the provider. Examples are provided to demonstrate the benefit and numerical results corroborate our premise that these personalized pricing plans can, in fact, increase provider revenue.

Data and Artificial Intelligence are changing the business models of many financial institutions. The availability and granularity of customer data allows for the development of a personalized banking experience which has been shown to improve customer relationships and increase retention. We present a Machine Learning approach to providing personalized overdraft protection. The approach simultaneously provides benefits to both customer and bank and hence increases customer retention while improving the bank's revenue. We illustrate the approach with examples.

Recent increases in the adoption of Online Social Networks (OSNs) for advertising has resulted in the research and development of algorithms that can maximize the resulting revenue. OSN users are likely to be influenced by their friends; therefore, one can leverage friendship relationships to determine how advertisements should be distributed among users. If a user is given an indication that their friend clicked on an advertisement link (called an impression), then they are more likely to also click on the impression if it were to be provided to them. The problem of assigning impressions can be modeled as an optimization problem in which the goal is to maximize the expected number of clicks achieved given a fixed number of impressions. Hosein and Lawrence [1] formulated this as a Stochastic Dynamic Programming problem in which impressions are provided in stages and the outcomes of previous stages are used in making impression allocations for the present stage. However, the determination of the optimal solution is computationally intractable for large problems; hence we require heuristics that are efficient while providing near-optimal solutions. In this paper we provide and compare various heuristics for this problem.