Modeling professional similarity by mining professional career trajectories

Xu, Ye; Zang, Li; Gupta, Abhishek; Bugdayci, Ahmet; Bhasin, Anmol

doi:10.1145/2623330.2623368

Cited by 42 publications

(20 citation statements)

References 19 publications

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“…Spertus et al [74], for example, measured how many users of the social network Orkut actually joined one of the recommended communities. In the case of LinkedIn, the authors of [81] report that user engagement was strongly increased when a new recommender for similar pro les was introduced. eir activity measures included both pro le views and email messages exchanged between recruiters and candidates.…”

Section: E Ects On Salesmentioning

confidence: 99%

Measuring the Business Value of Recommender Systems

Jannach

Jugovac

2019

ACM Trans. Manage. Inf. Syst.

183

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Recommender Systems are nowadays successfully used by all major web sites-from e-commerce to social media-to lter content and make suggestions in a personalized way. Academic research largely focuses on the value of recommenders for consumers, e.g., in terms of reduced information overload. To what extent and in which ways recommender systems create business value is, however, much less clear, and the literature on the topic is sca ered. In this research commentary, we review existing publications on eld tests of recommender systems and report which business-related performance measures were used in such real-world deployments. We summarize common challenges of measuring the business value in practice and critically discuss the value of algorithmic improvements and o ine experiments as commonly done in academic environments. Overall, our review indicates that various open questions remain both regarding the realistic quanti cation of the business e ects of recommenders and the performance assessment of recommendation algorithms in academia.

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Section: E Ects On Salesmentioning

confidence: 99%

Measuring the Business Value of Recommender Systems

Jannach

Jugovac

2019

ACM Trans. Manage. Inf. Syst.

183

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“…Users' career paths have also been utilized to model professional similarity for use in job recruitment process [24]. In this work, a sequence alignment method was used to quantify similarity between two career paths.…”

Section: Related Workmentioning

confidence: 99%

Talent Flow Analytics in Online Professional Network

Oentaryo¹,

Lim

Ashok

et al. 2018

Data Sci. Eng.

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Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are conducted on job seekers and employers to study job hop behavior. Beyond surveys, job hop behavior can also be studied in a highly scalable and timely manner using a data driven approach in response to fast-changing job landscape. Fortunately, the advent of online professional networks (OPNs) has made it possible to perform a large-scale analysis of talent flow. In this paper, we present a new data analytics framework to analyze the talent flow patterns of close to 1 million working professionals from three different countries/regions using their publicly-accessible profiles in an established OPN. As OPN data are originally generated for professional networking applications, our proposed framework re-purposes the same data for a different analytics task. Prior to performing job hop analysis, we devise a job title normalization procedure to mitigate the amount of noise in the OPN data. We then devise several metrics to measure the amount of work experience required to take up a job, to determine that the duration of a job's existence (also known as the job age), and the correlation between the above metric and propensity of hopping. We also study how job hop behavior is related to job promotion/demotion. Lastly, we perform

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“…In [12], Koren combines collaborative filtering with latent factors to utilize both the neighborhood information and latent factors. In our own previous work [29], we use such a technique to define career-path similarity for similar people recommendation system on LinkedIn. For a comprehensive coverage of the topic, please refer to the survey [26].…”

Section: Item-to-item Recommendationmentioning

confidence: 99%

From Query-By-Keyword to Query-By-Example

Ha-Thuc

Yan

et al. 2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position. To improve search efficiency, we propose the next generation of talent search at LinkedIn, also referred to as Search By Ideal Candidates. In this system, a searcher provides one or several ideal candidates as the input to hire for a given position. The system then generates a query based on the ideal candidates and uses it to retrieve and rank results. Shifting from the traditional QueryBy-Keyword to this new Query-By-Example system poses a number of challenges: How to generate a query that best describes the candidates? When moving to a completely different paradigm, how does one leverage previous product logs to learn ranking models and/or evaluate the new system with no existing usage logs? Finally, given the different nature between the two search paradigms, the ranking features typically used for Query-By-Keyword systems might not be optimal for Query-By-Example. This paper describes our approach to solving these challenges. We present experimental results confirming the effectiveness of the proposed solution, particularly on query building and search ranking tasks. As of writing this paper, the new system has been available to all LinkedIn members.

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Modeling professional similarity by mining professional career trajectories

Cited by 42 publications

References 19 publications

Measuring the Business Value of Recommender Systems

Measuring the Business Value of Recommender Systems

Talent Flow Analytics in Online Professional Network

From Query-By-Keyword to Query-By-Example

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