188/4 E-Commerce Group
Institute of Software Technology and Interactive Systems
Vienna University of Technology
Favoritenstrasse 9-11/188, A-1040 Vienna, Austria

Job-Empfehlungsdienst durch implizite Benutzerpräferenzen

Master Thesis
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Dorn, J.
Recommender systems assist individual users in finding the right items in large option space. Absolventen.at, an Austrian job board for graduates, uses such a system for rec- ommending appropriate jobs to applicants. So far, this system has only considered the resume as input for the user profile, which is compared with the available jobs. How- ever, only around half of the registered job seekers fill out the resume, for the other half no personalized recommendations can be generated. To improve this, the recom- mender system has been enhanced with implicit relevance feedback and the impacts of this approach have been examined in this thesis. Implicit feedback can be captured in an unobtrusive way and allows the system to infer user preferences. Four different user actions for implicit feedback have been identified on Absolventen.at, including reading of a job description, bookmarking, applying and searching for jobs. All of them provide different levels of evidence for interest, as an application is a more reliable indicator for interest than just reading a job description, which is taken into account with individual weighting parameters. In addition to that, gradual forgetting factors are used for adapt- ing the profile over time. All of this information is included in the hybrid user profile, which is represented as hyperdimensional vector and calculated by a linear combination of the resume and the preferred jobs. To evaluate the new approach, the preferred jobs of 46 job seekers were compared with the recommendations. The results show that including implicit feedback helps to increase the user coverage, as well as the accuracy of the recommendations.
Oct 2010