Content-Based Cross-Domain Recommendations Using Segmented Models
Shaghayegh Sahebi, Trevor Walker · 2014
Cross-Domain Recommendation is a new field of study in the area of recommender systems. The goal of this type of rec-ommender systems is to use information from other source domains to provide recommendations in target domains. In this work, we provide a generic framework for content-based cross-domain recommendations that can be used with var-ious classifiers. In this framework, we propose an ecient method of feature augmentation to implement adaptation of domains. Instead of defining the notion of domain based on item descriptions, we introduce user-based domains. We define meta-data features as a set of features to characterize the fields that domains come from and introduce indicator features to segment users into di↵erent domains based on values of the meta-data features. We study an implementa-tion of our framework based on logistic regression and per-form experiments on a dataset from LinkedIn to perform job recommendations. Our results show promising performance in certain domains of the data. 1.