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

An Empirical Comparison of Dissimilarity Measures for Recommender Systems

Many content-based recommendation approaches are based on a dissimilarity measure based on the product attributes. In this paper, we evaluate four dissimilarity measures for product recommendation using an online survey. In this survey, we asked users to specify which products they considered to be relevant recommendations given a reference product. We used microwave ovens as product category. Based on these responses, we create a relative relevance matrix we use to evaluate the dissimilarity measures with. Also, we use this matrix to estimate weights to be used in the dissimilarity measures. In this way, we evaluate four dissimilarity measures: the Euclidean Distance, the Hamming Distance, the Heterogeneous Euclidean-Overlap Metric, and the Adapted Gower Coefficient. The evaluation shows that these weights improve recommendation performance. Furthermore, the experiments indicate that when recommending a single product, the Heterogeneous Euclidean-Overlap Metric should be used and when recommending more than one product the Adapted Gower Coefficient is the best alternative. Finally, we compare these dissimilarity measures with a collaborative method and show that this method performs worse than the dissimilarity based approaches