Our research aims to bridge the gap between different web- sites to provide cross-site recommendations based on browsers. Recent advances have made recommender systems essential to various online applications, such as e-commerce, social networks, and review service websites. However, practical systems mainly focus on recommending inner-site homoge- neous items. For example, a movie review website usually recommends other movies within the site when a user has enjoyed a movie online. However, it would be exciting if the system recommends some attractive products related to this movie from some e-commerce websites like Amazon or eBay. Such an ability to provide heterogeneous cross-site rec- ommendations may shed light on brand new and promising business models, which could benefit both the online shops in expanding the marketing efforts, and the online users in discovering items of interest from a wider scope. In this re- search, we propose and formalize the problem of universal recommendation, record and analyze user browsing actions in web browsers, and provide browser-oriented cross-site rec- ommendations when the users are surfing online.