Hiring a secretary from a poset

Kumar, Ravi; Lattanzi, Silvio; Vassilvitskii, Sergei; Vattani, Andrea

doi:10.1145/1993574.1993582

Cited by 20 publications

(13 citation statements)

References 16 publications

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“…Another interesting line of work consider ground sets with a partial order [Georgiou et al 2008;Kumar et al 2011] (as opposed to the full order assumed in the classical problem). Partial order implies that the algorithm cannot compare every pair of two secretaries.…”

Section: Related Workmentioning

confidence: 99%

Interviewing secretaries in parallel

Feldman

Tennenholtz

2012

Proceedings of the 13th ACM Conference on Electronic Commerce

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Motivated by the parallel nature of on-line internet help-desks and human inspections, we introduce the study of interviewing secretaries in parallel, extending upon the study of the classical secretary problem. In our setting secretaries arrive into multiple queues, and are interviewed in parallel, with the aim of recruiting several secretaries in a timely manner. We consider a variety of new problems that fit this setting, and provide both upper and lower bounds on the efficiency of the corresponding interviewing policies, contrasting them with the classical single queue setting.

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Section: Related Workmentioning

confidence: 99%

Interviewing secretaries in parallel

Feldman

Tennenholtz

2012

Proceedings of the 13th ACM Conference on Electronic Commerce

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“…Such research includes work related to estimation of employee reputation for optimal hiring decisions [15], as well as work related to ranking and relevance aspects of job matching in labor marketplaces [16]. There has been work related to the theory of optimal hiring process, e.g., on the problem of finding the right hire for a job (the hiring problem), as well as on the classical secretary problem, where a growing company continuously interviews and decides whether to hire applicants [17,18]. Authors of [19] investigated job marketplace as a two-sided matching market using locally stable matching algorithms for solving the problem of finding a new job using social contacts.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, real life experience suggests that most candidates do not specify a lot of information in the on [6]{line forms and hence Candidate Meta{data is often incomplete} Once the applications of prospective candidates are received, they are subjected to careful scrutiny by a set of dedicated screeners. This screening process [17] is crucial because it directly affects the quality of the intake and hence, the company profits. The screeners typically proceed as below [18]:…”

Section: Introductionmentioning

confidence: 99%

Employment Recommendation System using Matching, Collaborative Filtering and Content Based Recommendation

Belsare¹,

Deshmukh²

2018

IJCATR

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Abstract:The tremendous growth of both information and usage has led to a so-called information overload problem in which users are finding it increasingly difficult to locate the right information at the right time Thus huge amount of information and easy access to it make recommender systems unavoidable [1]. We use recommender system every day without realizing it and without knowing what exactly happens. Recommender systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things he/she has never experienced. Benefits of recommender systems to the businesses using them include: The ability to offer unique personalized service for the customer, Increase trust and customer loyalty, Increase sales, click-through rates, conversions, etc., Opportunities for promotion, persuasion and Obtain more knowledge about customers. Recommender systems are software tools and techniques providing suggestions for items to be of use to a user. Job recommender systems are desired to attain a high level of accuracy while making the predictions which are relevant to the customer, as it becomes a very tedious task to explore thousands of jobs, posted on the web, periodically. Although a lot of job recommender systems [2] exist that use different strategies, here efforts have been put to make the job recommendations on the basis of candidates profile matching as well as preserving candidates job behavior or preferences. Firstly, the rules predicting the general preferences of the different user groups are mined. Then the job recommendations to the target candidate are made on the basis of content based matching as well as candidate preferences, which are preserved either in the form of mined rules or obtained by candidates own applied job history.

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“…(The best-choice problem on posets with restricted a priori knowledge about an underlying partial order was considered in a number of papers; for further bibliography and results, see [2], [3], [5], [8], [11], and [14].) In this paper we always assume that a poset has the greatest element (see the remark below).…”

Section: Mia Processmentioning

confidence: 99%

Monotone Case for an Extended Process

Kuchta

Morayne

2014

Adv. Appl. Probab.

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We consider a nonnegative discrete time and bounded horizon process X for which 0 is an absorbing state and extend it by a random variable that is independent of X. We find a sufficient condition for the resulting process to satisfy, after a canonical time rescaling, the hypothesis of the monotone case theorem. If X describes a secretary type search on a poset with one maximal element or if we consider X with no extension then this condition assumes an especially simple log-concavity type form.

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Hiring a secretary from a poset

Cited by 20 publications

References 16 publications

Interviewing secretaries in parallel

Interviewing secretaries in parallel

Employment Recommendation System using Matching, Collaborative Filtering and Content Based Recommendation

Monotone Case for an Extended Process

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