David Hardtke

User Latent Preference Model for Better Downside Management in Recommender Systems

Downside management is an important topic in the field of recommender systems. User satisfaction increases when good items are recommended, but satisfaction drops significantly when bad recommendations are pushed to them. For example, a parent would be disappointed if violent movies are recommended to their kids and may stop using the recommendation system entirely. A vegetarian would feel steak-house recommendations useless. A CEO in a mid-sized company would feel offended by receiving intern-level job recommendations. Under circumstances where there is penalty for a bad recommendation, a bad recommendation is worse than no recommendation at all. While most existing work focuses on upside management (recommending the best items to users), this paper emphasizes on achieving better downside management (reducing the recommendation of irrelevant or offensive items to users). The approach we propose is general and can be applied to any scenario or domain where downside management is key to the system. To tackle the problem, we design a user latent preference model to predict the user preference in a specific dimension, say, the dietary restrictions of the user, the acceptable level of adult content in a movie, or the geographical preference of a job seeker. We propose to use multinomial regression as the core model and extend it with a hierarchical Bayesian framework to address the problem of data sparsity. After the user latent preference is predicted, we leverage it to filter out downside items. We validate the soundness of our approach by evaluating it with an anonymous job application dataset on LinkedIn. The effectiveness of the latent preference model was demonstrated in both offline experiments and online A/B testings. The user latent preference model helps to improve the VPI (views per impression) and API (applications per impression) significantly which in turn achieves a higher user satisfaction.

Volunteer Experience May Not Bridge Gaps in Employment

This study investigated whether volunteer experience compensates for a gap in employment that occurs either early or late in ones career. Recruiters (n = 82) evaluated resumes of fictitious applicants with either early or late employment gaps, plus one of three types of volunteer experience: career‐related, career‐unrelated, and none. For applicants with an employment gap, resumes with volunteer experience – regardless of its career‐relatedness – were not rated significantly higher than resumes without volunteer experience. Although not statistically significant, resumes with late employment gaps were rated highest when they had career‐related volunteer experience and lowest when they had no volunteer experience. In line with human capital theory, applicants late in their career were rated higher than applicants early in their career.

Using social data for resume job matching

Bright has built an automated system for ranking job candidates against job descriptions. The candidates resume and social media profiles are interwoven to build an augmented user profile. Similarly, the job description is augmented by external databases and user-generated content to build an enhanced job profile. These augmented user and job profiles are then analyzed in order to develop numerical overlap features each with strong discriminating power, and in sum with maximal coverage. The resulting feature scores are then combined into a single Bright Score using a custom algorithm, where the feature weights are derived from a nation-wide and controlled study in which we collected a large sample of human judgments on real resume-job pairings. We demonstrate that the addition of social media profile data and external data improves the classification accuracy dramatically in terms of identifying the most qualified candidates.