The ever-growing amount of data available on the Internet calls for personalization. Yet, the most effective personalization schemes, such as those based on collaborative filtering (CF), are notoriously resource greedy. This paper presents HyRec, an online cost-effective scalable system for user-based CF personalization. HyRec offloads recommendation tasks onto the web browsers of users, while a server orchestrates the process and manages the relationships be-tween user profiles. HyRec has been fully implemented and extensively evaluated on several workloads from MovieLens and Digg. We convey the abil-ity of HyRec to reduce the operation costs of content providers by nearly 50% and to provide a 100-fold improvement in scala-bility with respect to a centralized (or cloud-based recommender approach), while preserving the quality of personalization. We also show that HyRec is virtually transparent to users and induces only 3% of the bandwidth consumption of a P2P solution.