Related papers: Flow-based reputation: more than just ranking
Aggregated data in real world recommender applications often feature fat-tailed distributions of the number of times individual items have been rated or favored. We propose a model to simulate such data. The model is mainly based on social…
Modern society functions on trust. The onchain economy, however, is built on the founding principles of trustless peer-to-peer interactions in an adversarial environment without a centralised body of trust and needs a verifiable system to…
This paper develops an axiomatic framework for ranking metrics, a general class of functionals for evaluating and ordering financial or insurance positions. Unlike traditional risk-adjusted performance measures-such as the Sharpe ratio,…
Many online marketplaces enjoy great success. Buyers and sellers in successful markets carry out cooperative transactions even if they do not know each other in advance and a moral hazard exists. An indispensable component that enables…
Fairness in recommender systems has been considered with respect to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue in a multistakeholder setting). Regardless, the concept has been commonly interpreted as some…
This paper addresses the challenges of evaluating network performance in the presence of fluctuating traffic patterns, with a particular focus on the impact of peak data rates on network resources. We introduce a set of metrics to quantify…
This paper presents a data-driven mean-field approach to model the popularity dynamics of users seeking public attention, i.e., influencers. We propose a novel analytical model that integrates individual activity patterns, expertise in…
With the emerging needs of creating fairness-aware solutions for search and recommendation systems, a daunting challenge exists of evaluating such solutions. While many of the traditional information retrieval (IR) metrics can capture the…
In the aftermath of the financial crisis, the growing literature on financial networks has widely documented the predictive power of topological characteristics (e.g. degree centrality measures) to explain the systemic impact or systemic…
Individual performance and reputation within a company are major factors that influence wage distribution, promotion and firing. Due to the complexity and collaborative nature of contemporary business processes, the evaluation of individual…
To manage the problem of having a higher demand for resources than availability of funds, research funding agencies usually rank the major research groups in their area of knowledge. This ranking relies on a careful analysis of the research…
With the explosive growth of accessible information, expecially on the Internet, evaluation-based filtering has become a crucial task. Various systems have been devised aiming to sort through large volumes of information and select what is…
A variety of rating-based recommendation methods have been extensively studied including the well-known collaborative filtering approaches and some network diffusion-based methods, however, social trust relations are not sufficiently…
The digital services economy consists of online platforms that facilitate interactions between service providers and consumers. This ecosystem is characterized by short-term, often one-off, transactions between parties that have no prior…
Commercial establishments like restaurants, service centres and retailers have several sources of customer feedback about products and services, most of which need not be as structured as rated reviews provided by services like Yelp, or…
A fundamental decision faced by a firm hiring employees - and a familiar one to anyone who has dealt with the academic job market, for example - is deciding what caliber of candidates to pursue. Should the firm try to increase its…
Information and knowledge in exchange in public networks is a crucial challenge that needs to be overcome in order to consolidate the benefits associated with such structures. We study the impact of the nature of the information exchanged…
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it is important to ask: what are the possible fairness risks,…
Peer-to-Peer systems are based on the concept of resources localization and mutualisation in dynamic context. In specific environment such as mobile networks, characterized by high variability and dynamicity of network conditions and…
Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require…