Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of rules for filtering job recommendations, wherein the rules are selected to maximize a reduction in negative outcomes associated with the job recommendations. Next, the system generates a label for a set of candidate-job pairs that match one or more of the rules and inputs the label with a set of candidate-job features for the set of candidate-job pairs as training data for a filtering model. The system then applies the filtering model to additional candidate-job features associated with a candidate and a set of jobs to produce a set of scores, wherein each score represents a likelihood that the candidate perceives a corresponding job as an undesirable recommendation. Finally, the system outputs a subset of the jobs as recommendations to the candidate based on the set of scores.

Abstract: A method for recommending products to a user includes providing a user profile with product related data. At least one bandit is generated to model product related recommendations. The bandit model(s) are passed to a recommendation module that provides recommendations to the user based on the bandit model and expected payoff. User interactions in response to the recommendation can be evaluated to adjust further recommendations.

Abstract: A method for presenting related products to a user includes providing product association data derived at least partially from at least one of traffic-based links and expert curated links. A product ensemble can be generated from the product association data. The generated product ensembles can be scored for compatibility and highly scored ensembles recommended to a user.