A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provide a vital technology for making the recruitment processes faster, more accurate and transparent. In this work, we present our research towards achieving a realistic matching approach for satisfactorily addressing this challenge. This novel approach relies on a matching learning solution aiming to learn from past solved cases in order to accurately predict the results in new situations. An empirical study shows us that our approach is able to beat solutions with no learning capabilities by a wide margin.Comment: 15 pages, 6 figure

Accurate and efficient profile matching in knowledge bases

A profile describes a set of properties, e.g. a set of skills a person may have, a set of skills required for a particular job, or a set of abilities a football player may have with respect to a particular team strategy. Profile matching aims to determine how well a given profile fits to a requested profile and vice versa. The approach taken in this article is grounded in a matching theory that uses filters in lattices to represent profiles, and matching values in the interval [0,1]: the higher the matching value the better is the fit. Such lattices can be derived from knowledge bases to represent the knowledge about profiles. An interesting question is, how human expertise concerning the matching can be exploited to obtain most accurate matchings. It will be shown that if a set of filters together with matching values by some human expert is given, then under some mild plausibility assumptions a matching measure can be determined such that the computed matching values preserve the relevant rankings given by the expert. A second question concerns the efficient querying of databases of profile instances. For matching queries that result in a ranked list of profile instances matching a given one it will be shown how corresponding top-k queries can be evaluated on grounds of pre-computed matching values. In addition, it will be shown how the matching queries can be exploited for gap queries that determine how profile instances need to be extended in order to improve in the rankings

The model theoretic notion of type in relational databases : a thesis presented in partial fulfillment of the requirements for the degree of Master of Information Sciences at Massey University, Wellington, New Zealand

Abstract. It is well known that finite model theory and database theory are two disciplines intimately connected. A topic which has been deeply studied in the context of finite model theory as well as in classic model theory, but which has not received the same attention in the context of databases, is the concept of type of a tuple. We believe that it is very important to have a clear understanding of the implications of the concept of type in the field of databases. Therefore, we study here the notion of type in FO (first-order logic) and in FOk (the restriction of FO to formulas with up to k variables), as well as in their corresponding infinitary extensions Lcow and L~w' respectively. The FOk types (L~w types) are quite relevant in database theory since they characterize the discerning power of the class of reflective relational machines of S. Abiteboul, C. Papadimitriou and V. Vianu with variable complexity k. We examine the three main characterizations of FO equivalence. These characterizations are based in Ehrenfeucht-Frai"sse games, back and forth systems of partial isomorphisms and isolating formulas for types. We study in detail all of them because they give a clear idea of the relevance of the notion of type in the context of databases, and make the fact that the types of the tuples in a database reflect all the information contained in it very clear. We found that the concept of type turned out to be useful to examine from a new perspective a well known problem of the field of databases, consisting on the redundant storage of information. Specifically, we initiate a study towards a method of normalization of relational databases based on the detection of redundant relations, i.e., relations which can be eliminated from the database without loosing information, by using isolating formulas for the types of the tuples in the database

A Smart Approach for Matching, Learning and Querying Information from the Human Resources Domain

We face the complex problem of timely, accurate and mutually satisfactory mediation between job offers and suitable applicant profiles by means of semantic processing techniques. In fact, this problem has become a major challenge for all public and private recruitment agencies around the world as well as for employers and job seekers. It is widely agreed that smart algorithms for automatically matching, learning, and querying job offers and candidate profiles will provide a key technology of high importance and impact and will help to counter the lack of skilled labor and/or appropriate job positions for unemployed people. Additionally, such a framework can support global matching aiming at finding an optimal allocation of job seekers to available jobs, which is relevant for independent employment agencies, e.g. in order to reduce unemployment