Building Trusted Startup Teams From LinkedIn Attributes: A Higher Order Probabilistic Analysis

Drakopoulos, Georgios; Kafeza, Eleanna; Mylonas, Phivos; Katheeri, Haseena Al

doi:10.1109/ictai50040.2020.00136

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…[30] propose a framework for assessing the trustworthiness of endorsements. The work of [31] proposes to measure the trustworthiness of job candidates based on their skills and endorsements. Based on this research, one line of future work is to incorporate the validation of endorsements into the RS to determine their veracity when taking them into account for recommendations.…”

Section: Discussionmentioning

confidence: 99%

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Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

2022

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Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content-based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web-based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained.

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Section: Discussionmentioning

confidence: 99%

“…To measure the level of skills. NO [31] 2020 Measure reliability of a candidate for LinkedIn profile startups.…”

Section: Refmentioning

confidence: 99%

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

2022

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“…However, more recently AI and ML algorithms have been deployed in scenarios that could have significant implications. For instance, algorithms are being used in hiring and promotion decisions [23], the criminal justice system [24],…”

Section: The Place Of Algorithms In Societymentioning

confidence: 99%

Trust in algorithms. An experimental approach

Marmolejo-Ramos

2023

Preprint

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Algorithms have become ubiquitous in our day-to-day activities. Their presence ranges from low-stakes decisions such as what music we should listen to, to high-stakes decisions regarding promotion decisions. Despite the frequency and range with which we interact with algorithms, academic research, as well as public outcries, suggest that humans often express distrust of algorithms and their decisions. Previous research suggests that there may be ways to mitigate this distrust through educational efforts that help explain how algorithms operate. With a sample of 1,841 participants across 19 countries, the present study investigates these notion by examining variations in algorithmic trust across low and high-stakes decisions, and the role of explainability and statistical literacy in shaping reactions. Broadly, results suggest a relationship between how much one understands about algorithms and their propensity to trust. Implicationsof these findings are discussed.

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“…Additional studies include qualitative characteristics of startups such as founder attributes, startup performance, and team dynamics to identify characteristics that may indicate red flags in developing a founding group [13][14][15][16]. On the other hand, studies may explore the characteristics of successful entrepreneurs [17], diving into personality traits such as stress tolerance and innovativeness.…”

Section: Introductionmentioning

confidence: 99%

Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches

Bai

Zhao

2021

Systems

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This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm.

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Building Trusted Startup Teams From LinkedIn Attributes: A Higher Order Probabilistic Analysis

Cited by 7 publications

References 37 publications

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

Trust in algorithms. An experimental approach

Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches

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