Matching While Learning

2021 ◽  
Author(s):  
Ramesh Johari ◽  
Vijay Kamble ◽  
Yash Kanoria
Keyword(s):  
Labor Market ◽  
Shadow Price ◽  
Cold Start ◽  
Bandit Problem ◽  
Different Types ◽  
The Future ◽  
Limited Supply ◽  
Cold Starts ◽  
Cold Start Problem ◽  
Multiarmed Bandit

Platforms face a cold start problem whenever new users arrive: namely, the platform must learn attributes of new users (explore) in order to match them better in the future (exploit). How should a platform handle cold starts when there are limited quantities of the items being recommended? For instance, how should a labor market platform match workers to jobs over the lifetime of the worker, given a limited supply of jobs? In this setting, there is one multiarmed bandit problem for each worker, coupled together by the constrained supply of jobs of different types. A solution is developed to this problem. It is found that the platform should estimate a shadow price for each job type, and for each worker, adjust payoffs by these prices (i) to balance learning with payoffs early on and (ii) to myopically match them thereafter.

scholarly journals Does the Market Value CEO Styles?

2016 ◽  
Vol 106 (5) ◽  
pp. 262-266 ◽  
Author(s):  
Antoinette Schoar ◽  
Luo Zuo
Keyword(s):  
Labor Market ◽  
Market Reaction ◽  
Market Value ◽  
Different Types ◽  
Limited Supply

We study how investors perceive the skill set that different types of CEOs bring into their companies. We compare CEOs who started their careers during a recession with other CEOs. We show that the announcement return around the appointment of a recession CEO is very significant and positive, and this positive market reaction is driven by cases where a recession CEO replaces a non-recession CEO. Our results indicate that the market assigns a positive and economically meaningful value to a recession CEO, suggesting that there is a limited supply of these types of CEOs in the executive labor market.

scholarly journals An embedded bandit algorithm based on agent evolution for cold-start problem

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rui Qiu ◽  
Wen Ji
Keyword(s):  
Evaluation Method ◽  
Contextual Information ◽  
Cold Start ◽  
Measurement Model ◽  
Bandit Problem ◽  
Content Type ◽  
Contextual Features ◽  
Interaction History ◽  
Practical Implications ◽  
Cold Start Problem

Purpose Many recommender systems are generally unable to provide accurate recommendations to users with limited interaction history, which is known as the cold-start problem. This issue can be resolved by trivial approaches that select random items or the most popular one to recommend to the new users. However, these methods perform poorly in many cases. This paper aims to explore the problem that how to make accurate recommendations for the new users in cold-start scenarios. Design/methodology/approach In this paper, the authors propose embedded-bandit method, inspired by Word2Vec technique and contextual bandit algorithm. The authors describe user contextual information with item embedding features constructed by Word2Vec. In addition, based on the intelligence measurement model in Crowd Science, the authors propose a new evaluation method to measure the utility of recommendations. Findings The authors introduce Word2Vec technique for constructing user contextual features, which improved the accuracy of recommendations compared to traditional multi-armed bandit problem. Apart from this, using this study’s intelligence measurement model, the utility also outperforms. Practical implications Improving the accuracy of recommendations during the cold-start phase can greatly raise user stickiness and increase user favorability, which in turn contributes to the commercialization of the app. Originality/value The algorithm proposed in this paper reflects that user contextual features can be represented by clicked items embedding vector.

Shortage of Skills in the Labor Market: Limited Supply, Lack of Demand, or False Signals from Employers?

2004 ◽  
pp. 76-94 ◽  
Author(s):  
V. Gimpelson
Keyword(s):  
Labor Market ◽  
Competitive Market ◽  
Skilled Workers ◽  
Industrial Enterprises ◽  
Russian Industry ◽  
General Skills ◽  
Limited Supply

The article discusses the issue of shortage of skills in the Russian industry. Using microdata from a survey of industrial enterprises, the author confirms that most of employers complain of difficulties in hiring and attaching skilled workers. In case of mass occupations, this shortage relates mostly to low efficient enterprises, which are unable or unwilling to pay competitive market going wage. More efficient and better paying firms are less likely to face shortage of general skills on the labor market but may face limited supply of specific skills.

DIGITALIZATION OF LABOR MIGRATION PROCESSES IN UZBEKISTAN. WORLD EXPERIENCE

2020 ◽  
Vol 4 (3) ◽  
pp. 29-39
Author(s):  
Sulkhiya Gazieva ◽  
Keyword(s):  
Labor Market ◽  
Technological Change ◽  
Labor Migration ◽  
Smart Phone ◽  
Logic Circuits ◽  
Digital Logic ◽  
The Internet ◽  
The Future

The future of labor market depends upon several factors, long-term innovation and the demographic developments. However, one of the main drivers of technological change in the future is digitalization and central to this development is the production and use of digital logic circuits and its derived technologies, including the computer,the smart phone and the Internet. Especially, smart automation will perhaps not cause e.g.regarding industries, occupations, skills, tasks and duties

scholarly journals Asteroid and Comet Surveys

1994 ◽  
Vol 161 ◽  
pp. 385-400
Author(s):  
B.G. Marsden
Keyword(s):  
Logical Sequence ◽  
Different Types ◽  
The Future ◽  
Ecliptic Latitude ◽  
Similarities And Differences

Past surveys are described in the logical sequence of (1) comets visually, (2) asteroids visually, (3) asteroids photographically and (4) comets photographically. Plots show the evolution of asteroid surveys in terms of visual discovery magnitude and ecliptic latitude, and similarities and differences between surveys for the different types of body are discussed. The paper ends with a brief discussion of more recent discovery methods and some thoughts on the future.

scholarly journals Project Narratives That Potentially Perform and Change the Future

2021 ◽  
pp. 875697282199534
Author(s):  
Natalya Sergeeva ◽  
Graham M. Winch
Keyword(s):  
Life Cycle ◽  
Value Creation ◽  
Future Research ◽  
Future Research Agenda ◽  
Project Life Cycle ◽  
Different Types ◽  
The Future ◽  
Organizational Narrative ◽  
Project Life ◽  
Future Project

This article develops a framework for applying organizational narrative theory to understand project narratives that potentially perform and change the future. Project narratives are temporal but often get repeated throughout the project life cycle to stabilize meaning, and could be about project mission, vision, identity, value creation, and so forth. Project narratives have important implications for organizational identity and image crafting. This article differentiates among different types of project narratives in relation to a project life cycle, providing case studies of project narratives on three major UK rail projects. We then set out the future research agenda into project narrative work.

scholarly journals EXPRESS: Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach

2021 ◽  
pp. 002224372110329
Author(s):  
Nicolas Padilla ◽  
Eva Ascarza
Keyword(s):  
Machine Learning ◽  
Cold Start ◽  
Customer Relationship ◽  
Exponential Families ◽  
Modeling Framework ◽  
Wide Range ◽  
The Moment ◽  
Probabilistic Machine Learning ◽  
Marketing Actions ◽  
Cold Start Problem

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

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