Entity Personalized Talent Search Models with Tree Interaction Features

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models which lack preference personalization in the user-level due to being trained only with globally collected recruiter activity data. In this paper, we propose an entity-personalized Talent Search model which utilizes a combination of generalized linear mixed (GLMix) models and gradient boosted decision tree (GBDT) models, and provides personalized talent recommendations using nonlinear tree interaction features generated by the GBDT. We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems. Finally, we provide offline and online experiment results benchmarking our entity-personalized model with tree interaction features, which demonstrate significant improvements in our precision metrics compared to globally trained non-personalized models.Comment: This paper has been accepted for publication at ACM WWW 201

PANEL: Challenges for multimedia/multimodal research in the next decade

The multimedia and multimodal community is witnessing an explosive transformation in the recent years with major societal impact. With the unprecedented deployment of multimedia devices and systems, multimedia research is critical to our abilities and prospects in advancing state-of-theart technologies and solving real-world challenges facing the society and the nation. To respond to these challenges and further advance the frontiers of the field of multimedia, this panel will discuss the challenges and visions that may guide future research in the next ten years

What’s Next in Loyalty Programs: Highlights of the 2014 Cornell Loyalty Program Management Roundtable

Loyalty programs have hit the maturity stage in the hospitality industries,” stated loyalty roundtable chair Michael McCall, as he opened the first session, “and many firms are now struggling to demonstrate the return on the investment in these programs and also to advance them to the next level. The goal of this roundtable is to discuss ways loyalty program executives can continue to extract value for these programs.” Held in Spring 2014 at the School of Hotel Administration at Cornell University, the roundtable brought together leading practitioners and researchers to examine ways to improve loyalty program management

Just in Time: The Beyond-the-Hype Potential of E-Learning

Based on a year of conversations with more than 100 leading thinkers, practitioners, and entrepreneurs, this report explores the state of e-learning and the potential it offers across all sectors of our economy -- far beyond the confines of formal education. Whether you're a leader, worker in the trenches, or just a curious learner, imagine being able to access exactly what you need, when you need it, in a format that's quick and easy to digest and apply. Much of this is now possible and within the next decade, just-in-time learning will likely become pervasive.This report aims to inspire you to consider how e-learning could change the way you, your staff, and the people you serve transfer knowledge and adapt over time

A Web-Based Recommendation System To Predict User Movements Through Web Usage Mining

Web usage mining has become the subject of exhaustive research, as its potential for Web based personalized services, prediction user near future intentions, adaptive Web sites and customer profiling is recognized. Recently, a variety of the recommendation systems to predict user future movements through web usage mining have been proposed. However, the quality of the recommendations in the current systems to predict users‘ future requests can not still satisfy users in the particular web sites. The accuracy of prediction in a recommendation system is a main factor which is measured as quality of the system. The latest contribution in this area achieves about 50% for the accuracy of the recommendations. To provide online prediction effectively, this study has developed a Web based recommendation system to Predict User Movements, named as WebPUM, for online prediction through web usage mining system and proposed a novel approach for classifying user navigation patterns to predict users‘ future intentions. There are two main phases in WebPUM; offline phase and online phase. The approach in the offline phase is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining. In this phase, an undirected graph based on the Web pages as graph vertices and degree of connectivity between web pages as weight of the graph is created by proposing new formula for weight of the each edge in the graph. Moreover, navigation pattern mining has been done by finding connected components in the graph. In the online phase, the longest common subsequence algorithm is used as a new approach in recommendation system for classifying current user activities to predict user next movements. The longest common subsequence is a well-known string matching algorithm that we have utilized to find the most similar pattern between a set of navigation patterns and current user activities for creating the recommendations

Creating a New Vision of the Urban High School

Provides an assessment of current school structure and explores the rational for changing the design of public education. Looks at recent efforts to organize and fund urban schools, in search of a solution to the problem of high school obsolescence

Re-Envisioning Talent Management for the 4th Industrial Revolution: A Systems and Design Thinking Intervention

This dissertation examines the application of interactive planning as an intervention for the purpose of exploring its effectiveness with diverse, cross-organizational stakeholders when considering an issue that transcends individual organizations. The case study offers a practitioner method and approach using systems and design thinking to re-envision talent management in the 4th Industrial Revolution. The first two phases of a three-phase model, entitled Consider, Research, Explore, Associate, Theorize, and Empathize, (C.R.E.A.T.E.) contains aspects of systems and design thinking, and are addressed in this study. Data were gathered from direct observation and facilitation of two stakeholder sessions. The first, in Blue Bell, Pennsylvania, included participants employed in a variety of roles from across industries and companies in the Greater Philadelphia area. The second, in Austin, Texas, included external human capital consultants across the United States and Canada who were all partner-members of Career Partners International (CPI). Results indicated that stakeholders representing different organizations, roles and boundaries can enter into generative space regarding a common issue. Results also show designs with emergent themes that have the potential to influence the creation of an effective talent management system, and the C.R.E.A.T.E. model can be applied to accelerate the pace of innovation and creative solution seeking with regard to issues of complexity. Reflections on the facilitation process and a timeline practitioners can use with internal and external clients are provided along with suggestions for future research into this highly collaborative and interactive process