Analysis of Probabilistic News Recommender Systems

The focus of this research is the N “most popular” (Top-N) news recommender systems (NRS), widely used by media sites (e.g. New York Times, BBC, Wall Street Journal all prominently use this). This common recommendation process is known to have major limitations in terms of creating artificial amplification in the counts of recommended articles and that it is easily susceptible to manipulation. To address these issues, probabilistic NRS has been introduced. One drawback of the probabilistic recommendations is that it potentially chooses articles to recommend that might not be in the current “best” list. However, the probabilistic selection of news articles is highly robust towards common manipulation strategies. This paper compares the two variants of NRS (Top-N and probabilistic) based on (1) accuracy loss (2) distortion in counts of articles due to NRS and (3) comparison of probabilistic NRS with an adapted influence limiter heuristic

News Recommender Systems with Feedback

The focus of present research is widely used news recommendation techniques such as “most popular” or “most e-mailed”. In this paper we have introduced an alternative way of recommendation based on feedback. Various notable properties of the feedback based recommendation technique have been also discussed. Through simulation model we show that the recommendation technique used in the present research allows implementers to have a flexibility to make a balance between accuracy and distortion. Analytical results have been established in a special case of two articles using the formulation based on generalized urn models. Finally, we show that news recommender systems can be also studied through two armed bandit algorithms

Applications of Agent Based Approaches in Business: A Three Essay Dissertation

The goal of this dissertation is to investigate the enabling role that agent based simulation plays in business and policy. The aforementioned issue has been addressed in this dissertation through three distinct, but related essays. The first essay is a literature review of different research applications of agent based simulation in various business disciplines, such as finance, economics, information systems, management, marketing and accounting. Various agent based simulation tools to develop computational models are discussed. The second essay uses an agent-based simulation approach to study important properties of the widely used most popular news recommender systems (NRS). This essay highlights the major limitations of most popular NRS in terms of: (i) susceptibility towards manipulation and (ii) unduly penalizing the article which may have just missed making the cutoff in most popular list. A probabilistic variant of recommendation has been introduced as an alternative to most popular list. Classical results from urn models are used to derive theoretical results for special cases, and to study specific properties of the probabilistic recommender. In addition to simulations, various statistical methodologies are used, such as regression based methodologies as part of a broader decision analysis tool. The third essay views firms as agents in building regression based empirical models to investigate the impact of outsourcing on firms. Using an economy wide panel data of outsourcing expenses of firms, the third essay first investigates the value addition by the IT backgrounds of project owners in managing IT related projects. Then it investigates the impact of peer-pressure on a firm\u27s outsourcing behavior

A complex systems perspective of news recommender systems: Guiding emergent outcomes with feedback models.

Algorithms are increasingly making decisions regarding what news articles should be shown to online users. In recent times, unhealthy outcomes from these systems have been highlighted including their vulnerability to amplifying small differences and offering less choice to readers. In this paper we present and study a new class of feedback models that exhibit a variety of self-organizing behaviors. In addition to showing important emergent properties, our model generalizes the popular "top-N news recommender systems" in a manner that provides media managers a mechanism to guide the emergent outcomes to mitigate potentially unhealthy outcomes driven by the self-organizing dynamics. We use complex adaptive systems framework to model the popularity evolution of news articles. In particular, we use agent-based simulation to model a reader's behavior at the microscopic level and study the impact of various simulation hyperparameters on overall emergent phenomena. This simulation exercise enables us to show how the feedback model can be used as an alternative recommender to conventional top-N systems. Finally, we present a design framework for multi-objective evolutionary optimization that enables recommendation systems to co-evolve with the changing online news readership landscape