Towards the Evaluation of Recommender Systems with Impressions

In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, search results, or business rules that may select specific products for recommendations. The fact that the user interacted with a specific item given a list of recommended ones may benefit from a richer interaction signal, in which some items the user did not interact with may be considered negative interactions. This work presents a preliminary evaluation of recommendation models with impressions. First, impressions are characterized by describing their assumptions, signals, and challenges. Then, an evaluation study with impressions is described. The study's goal is two-fold: to measure the effects of impressions data on properly-tuned recommendation models using current open-source datasets and disentangle the signals within impressions data. Preliminary results suggest that impressions data and signals are nuanced, complex, and effective at improving the recommendation quality of recommenders. This work publishes the source code, datasets, and scripts used in the evaluation to promote reproducibility in the domain

PrivateJobMatch: A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment

Coordination failure reduces match quality among employers and candidates in the job market, resulting in a large number of unfilled positions and/or unstable, short-term employment. Centralized job search engines provide a platform that connects directly employers with job-seekers. However, they require users to disclose a significant amount of personal data, i.e., build a user profile, in order to provide meaningful recommendations. In this paper, we present PrivateJobMatch -- a privacy-oriented deferred multi-match recommender system -- which generates stable pairings while requiring users to provide only a partial ranking of their preferences. PrivateJobMatch explores a series of adaptations of the game-theoretic Gale-Shapley deferred-acceptance algorithm which combine the flexibility of decentralized markets with the intelligence of centralized matching. We identify the shortcomings of the original algorithm when applied to a job market and propose novel solutions that rely on machine learning techniques. Experimental results on real and synthetic data confirm the benefits of the proposed algorithms across several quality measures. Over the past year, we have implemented a PrivateJobMatch prototype and deployed it in an active job market economy. Using the gathered real-user preference data, we find that the match-recommendations are superior to a typical decentralized job market---while requiring only a partial ranking of the user preferences.Comment: 45 pages, 28 figures, RecSys 201

Artificial Neural Network system applied to Human Resource Management

In this project we study how Artificial Neural Network can be applied to Human Resources by supporting users from XING, a career-oriented social network, to find the desired job, and also recruiters that use this resource to find the best candidate for a given job. Specially, an ANN is created, using NN Toolbox in Matlab, with the purpose of predicting those users that given an specific job offer are likely to interact by performing a bookmark or reply on the job post recommended. The sample used contains 714.800 user-item pairs with 19 inputs that give information about the users and the job posts and 1 output that determines wether the user has positively interact with the job offer or not. The ANN created is a two layer feed-forward network that uses Levenberg-Marquardt backpropagation algorithm and with the correct number of neurons on the hidden layer does a good prediction. Further research can be done to improve Human Resource Management using data mining