Conceptualizing clusters through the lens of networks: a critical synthesis

Clusters, as spatial concentrations of economic activity, constitute an important form of coordination with significant repercussions in the configuration of firm and territorial strategies. They are recognized, both by academics and policymakers, as a territorial pattern of economy yielding critical issues in terms of competitive advantage, innovation, and economic growth. Despite that, a rigorous and clear-cut definition of cluster is still far from being reached. In the present paper, resorting to a critical synthesis of the literature on networks and clusters, we propose a unified, encompassing, and less blurred definition of cluster.Clusters, Networks, Concepts

The Many Faces of Lifelong Learning: Recent Education Policy Trends in Europe

This article examines the rise of the discourse on lifelong learning across Europe and the variety of national policy trends which its rhetoric occludes. The ubiquitous presence of this meta-discourse in education and training policy-in-theory is seen as a singular event which can be ascribed to the impact of the variety of global forces on the education arena. It serves specific political functions in addition to signalling real changes in education and training systems. The duality of convergent rhetoric and divergent policy- in- practise is seen as a challenge to education policy analysis which requires multi-layered interpretation

The social foundations of the bureaucratic order

This article views the bureaucratic form of organization as both an agent and an expression of key modern social innovations that are most clearly manifested in the non-inclusive terms by which individuals are involved in organizations. Modern human involvement in organizations epitomizes and institutionally embeds the crucial yet often overlooked cultural orientation of modernity whereby humans undertake ac-tion along well-specified and delimited paths thanks to their capacity to isolate and suspend other personal or social considerations. The organizational involvement of humans qua role agents rather than qua persons helps unleash formal organizing from being tied to the indolence of the human body and the languish process of per-sonal or psychological reorientation. Thanks to the loosening of these ties, the bu-reaucratic organization is rendered capable to address the shifting contingencies un-derlying modern life by reshuffling and re-assembling the roles and role patterns by which it is made. The historically unique adaptive capacity of bureaucracy remains though hidden behind the ubiquitous presence of routines and standard operating procedures –requirements for the standardization of roles– that are mistakenly ex-changed for the essence of the bureaucratic form

Salience and Market-aware Skill Extraction for Job Targeting

At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs You May Be Interested In (JYMBII) system to match the best candidates with their dream jobs. One of the most challenging tasks for developing these systems is to properly extract important skill entities from job postings and then target members with matched attributes. In this work, we show that the commonly used text-based \emph{salience and market-agnostic} skill extraction approach is sub-optimal because it only considers skill mention and ignores the salient level of a skill and its market dynamics, i.e., the market supply and demand influence on the importance of skills. To address the above drawbacks, we present \model, our deployed \emph{salience and market-aware} skill extraction system. The proposed \model ~shows promising results in improving the online performance of job recommendation (JYMBII) ($+1.92\%$ job apply) and skill suggestions for job posters ($-37\%$ suggestion rejection rate). Lastly, we present case studies to show interesting insights that contrast traditional skill recognition method and the proposed \model~from occupation, industry, country, and individual skill levels. Based on the above promising results, we deployed the \model ~online to extract job targeting skills for all $20$M job postings served at LinkedIn.Comment: 9 pages, to appear in KDD202

Context-aware OLAP for textual data warehouses

Decision Support Systems (DSS) that leverage business intelligence are based on numerical data and On-line Analytical Processing (OLAP) is often used to implement it. However, business decisions are increasingly dependent on textual data as well. Existing research work on textual data warehouses has the limitation of capturing contextual relationships when comparing only strongly related documents. This paper proposes an Information System (IS) based context-aware model that uses word embedding in conjunction with agglomerative hierarchical clustering algorithms to dynamically categorize documents in order to form the concept hierarchy. The results of the experimental evaluation provide evidence of the effectiveness of integrating textual data into a data warehouse and improving decision making through various OLAP operations

Relationship recommender system in a business and employment-oriented social network

[EN] In the last ten years, social networks have had a great influence on people’s lifestyles and have changed, above all, the way users communicate and relate. This is why, one of the main lines of research in the field of social networks focuses on finding and analyzing possible connections between users. These developments allow users to expand on their network of contacts without having to search among the total set of users. However, there are many types of social networks which attract users with specific needs, these needs influence on the type of contacts users are looking for. Our article proposes a relationship recommender system for a business and employment-oriented social network. The presented system functions by extracting relevant information from the social network which it then uses to adequately recommend new contacts and job offers to users. The recommender system uses information gathered from job offer descriptions, user profiles and users’ actions. Then, different metrics are applied in order to discover new ties that are likely to convert into relationships