54,330 research outputs found
ONTOLOGY BASED TECHNICAL SKILL SIMILARITY
Online job boards have become a major platform for technical talent procurement and job search. These job portals have given rise to challenging matching and search problems. The core matching or search happens between technical skills of the job requirements and the candidate\u27s profile or keywords. The extensive list of technical skills and its polyonymous nature makes it less effective to perform a direct keyword matching. This results in substandard job matching or search results which misses out a closely matching candidate on account of it not having the exact skills. It is important to use a semantic similarity measure between skills to improve the relevance of the results. This paper proposes a semantic similarity measure between technical skills using a knowledge based approach. The approach builds an ontology using DBpedia and uses it to derive a similarity score. Feature based ontology similarity measures are used to derive a similarity score between two skills. The ontology also helps in resolving a base skill from its multiple representations. The paper discusses implementation of custom ontology, similarity measuring system and performance of the system in comparing technical skills. The proposed approach performs better than the Resumatcher system in finding the similarity between skills. Keywords
Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start
Every day, thousands of users sign up as new Wikipedia contributors. Once
joined, these users have to decide which articles to contribute to, which users
to seek out and learn from or collaborate with, etc. Any such task is a hard
and potentially frustrating one given the sheer size of Wikipedia. Supporting
newcomers in their first steps by recommending articles they would enjoy
editing or editors they would enjoy collaborating with is thus a promising
route toward converting them into long-term contributors. Standard recommender
systems, however, rely on users' histories of previous interactions with the
platform. As such, these systems cannot make high-quality recommendations to
newcomers without any previous interactions -- the so-called cold-start
problem. The present paper addresses the cold-start problem on Wikipedia by
developing a method for automatically building short questionnaires that, when
completed by a newly registered Wikipedia user, can be used for a variety of
purposes, including article recommendations that can help new editors get
started. Our questionnaires are constructed based on the text of Wikipedia
articles as well as the history of contributions by the already onboarded
Wikipedia editors. We assess the quality of our questionnaire-based
recommendations in an offline evaluation using historical data, as well as an
online evaluation with hundreds of real Wikipedia newcomers, concluding that
our method provides cohesive, human-readable questions that perform well
against several baselines. By addressing the cold-start problem, this work can
help with the sustainable growth and maintenance of Wikipedia's diverse editor
community.Comment: Accepted at the 13th International AAAI Conference on Web and Social
Media (ICWSM-2019
Similarity and the trustworthiness of distributive judgements
When people must either save a greater number of people from a smaller harm or a smaller number from a greater harm, do their choices reflect a reasonable moral outlook? We pursue this question with the help of an experiment. In our experiment, two-fifths of subjects employ a similarity heuristic. When alternatives appear dissimilar in terms of the number saved but similar in terms of the magnitude of harm prevented, this heuristic mandates saving the greater number. In our experiment, this leads to choices that are inconsistent with all standard theories of justice. We argue that this demonstrates the untrustworthiness of distributive judgments in cases that elicit similarity-based choice
The role of cultural value dimensions in relational demography
The Malaysian public sector has undergone various transformations since the Independence. From its custodial role in the newly independent country, the public sector had changed and taken an active role in the country’s economic development. However, since 1980s onwards, the philosophy and techniques of New Public Management (NPM) have been implemented in Malaysia.This again transformed the public sector from being an engine of economic growth to become a facilitator to the private sector and service provider to the public. In line with NPM’s underlying belief of the superiority of businesslike practices, various contemporary management practices and philosophy
were implemented in the Malaysian public sector. The implantation of private sector practices in the public sector was enhanced with the introduction a performance measurement system which utilises the use of key performance
indicators in 2005. Thus, the purpose of this paper is to examine and analyse the current improvement programme within the wider public sector reform programmes in Malaysia. The issues and consequences of using key
performance indicators in the public sector are discussed. To understand further the reasons and the push for reform, contextual descriptions of the various phases of public sector reform in Malaysia are also discussed in this paper
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
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