3,096 research outputs found
Cluster Hire in Social Networks Using Modified Weighted Structural Clustering Algorithm for Networks (MWSCAN)
The concept of effective collaboration within a group is immensely used in organizations as a viable means for improving team performance. Any organization or prominent institute, who works with multiple projects needs to hire a group of experts who can complete a set of projects. When hiring a group of experts, numerous considerations must be taken into account. In the Cluster Hire problem, we are given a set of experts, each having a set of skills. Also, we are given a set of projects, each requiring a set of skills. Upon completion of each project, a profit is generated for an organization. Each expert demands a monetary cost (i.e., salary) to provide his/her expertise in projects. The Cluster Hire problem can be solved by hiring a group of experts for a set of projects within the constraints of a budget for hiring and a working capacity of each expert. An extension to this problem is assuming there exists a social network amongst the experts, which contains their past collaboration information. If two experts have collaborated in the past, then they are preferred to be on the same team in the future. The goal of our research is to find a collaborative group of experts who can work effectively together to complete a set of projects. Currently, the solution to the Cluster Hire problem in social networks is achieved using greedy heuristic algorithms and Integer Linear Programming (ILP) approach. Greedy algorithms often generate fast results, but they make locally optimal choices at each step and do not produce global optimal results. The drawbacks of the ILP approach are that it requires a considerable amount of memory for the creation of variables and constraints and also has a very high processing time for large networks. Whereas, Weighted Structural Clustering Algorithm for Networks (WSCAN) has been proved to produce faster results for Team Formation Problem (i.e., hiring a team of experts for a single project), which is a special case of Cluster Hire problem. We are proposing to solve the Cluster Hire problem in social networks using Modified Weighted Structural Clustering Algorithm for Networks (MWSCAN). We run our experiments on a large dataset of 50K experts. ILP is not capable of working with such large networks. Therefore, we will be comparing our results with the greedy heuristic solution. Our findings indicate that the MWSCAN algorithm generates more efficient results in terms of the number of projects completed and profit produced for the given budget compared to the greedy heuristic algorithm to solve the Cluster Hire problem in social networks
Cluster Hire in Social Networks Using Modified Weighted Structural Clustering Algorithm for Networks (MWSCAN)
The concept of effective collaboration within a group is immensely used in organizations as a viable means for improving team performance. Any organization or prominent institute, who works with multiple projects needs to hire a group of experts who can complete a set of projects. When hiring a group of experts, numerous considerations must be taken into account. In the Cluster Hire problem, we are given a set of experts, each having a set of skills. Also, we are given a set of projects, each requiring a set of skills. Upon completion of each project, a profit is generated for an organization. Each expert demands a monetary cost (i.e., salary) to provide his/her expertise in projects. The Cluster Hire problem can be solved by hiring a group of experts for a set of projects within the constraints of a budget for hiring and a working capacity of each expert. An extension to this problem is assuming there exists a social network amongst the experts, which contains their past collaboration information. If two experts have collaborated in the past, then they are preferred to be on the same team in the future. The goal of our research is to find a collaborative group of experts who can work effectively together to complete a set of projects. Currently, the solution to the Cluster Hire problem in social networks is achieved using greedy heuristic algorithms and Integer Linear Programming (ILP) approach. Greedy algorithms often generate fast results, but they make locally optimal choices at each step and do not produce global optimal results. The drawbacks of the ILP approach are that it requires a considerable amount of memory for the creation of variables and constraints and also has a very high processing time for large networks. Whereas, Weighted Structural Clustering Algorithm for Networks (WSCAN) has been proved to produce faster results for Team Formation Problem (i.e., hiring a team of experts for a single project), which is a special case of Cluster Hire problem. We are proposing to solve the Cluster Hire problem in social networks using Modified Weighted Structural Clustering Algorithm for Networks (MWSCAN). We run our experiments on a large dataset of 50K experts. ILP is not capable of working with such large networks. Therefore, we will be comparing our results with the greedy heuristic solution. Our findings indicate that the MWSCAN algorithm generates more efficient results in terms of the number of projects completed and profit produced for the given budget compared to the greedy heuristic algorithm to solve the Cluster Hire problem in social networks
Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design
The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface
A survey on online active learning
Online active learning is a paradigm in machine learning that aims to select
the most informative data points to label from a data stream. The problem of
minimizing the cost associated with collecting labeled observations has gained
a lot of attention in recent years, particularly in real-world applications
where data is only available in an unlabeled form. Annotating each observation
can be time-consuming and costly, making it difficult to obtain large amounts
of labeled data. To overcome this issue, many active learning strategies have
been proposed in the last decades, aiming to select the most informative
observations for labeling in order to improve the performance of machine
learning models. These approaches can be broadly divided into two categories:
static pool-based and stream-based active learning. Pool-based active learning
involves selecting a subset of observations from a closed pool of unlabeled
data, and it has been the focus of many surveys and literature reviews.
However, the growing availability of data streams has led to an increase in the
number of approaches that focus on online active learning, which involves
continuously selecting and labeling observations as they arrive in a stream.
This work aims to provide an overview of the most recently proposed approaches
for selecting the most informative observations from data streams in the
context of online active learning. We review the various techniques that have
been proposed and discuss their strengths and limitations, as well as the
challenges and opportunities that exist in this area of research. Our review
aims to provide a comprehensive and up-to-date overview of the field and to
highlight directions for future work
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.Comment: 30 pages, 15 figure
Knowledge aggregation in people recommender systems : matching skills to tasks
People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecà rrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessà ria. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implÃcita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explÃcita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecà rrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les à rees d’interès per a caracteritzar les persones es dedueixen dels seus currÃculums i s’expressen en termes d’incertesa evitant que els interessos es demanin explÃcitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implÃcita i atributs indicats explÃcitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les à rees d'aplicació futures i noves perspectives.Postprint (published version
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