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.

In many domains of information retrieval, we are required to retrieve documents that describe requirements on a predefined set of terms. A requirement is a relationship between a set of terms and the document. As requirements become more complex by catering for optional, alternative, and combinations of terms, efficiently retrieving documents becomes more challenging due to the exponential size of the search space. In this paper, we propose RevBoMIR, which utilizes a modified Boolean Model for Information Retrieval to retrieve requirements-based documents without sacrificing the expressiveness of requirements. Our proposed approach is particularly useful in domains where documents embed criteria that can be satisfied by mandatory, alternative or disqualifying terms to determine its retrieval. Finally, we present a graph model for representing document requirements, and demonstrate Requirement Search via a university degree search application.

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.