Popular search engines essentially rely on information about the structure of the graph of linked elements to find the most relevant results for a given query. While this approach is satisfactory for popular interest domains or when the user expectations follow the main trend, it is very sensitive to the case of ambiguous queries, where queries can have answers over several different domains. Elements pertaining to an implicitly targeted interest domain with low popularity are usually ranked lower than expected by the user. This is a consequence of the poor usage of user-centric information in search engines. Leveraging semantic information can help avoid such situations by proposing complementary results that are carefully tailored to match user interests. This paper proposes a collaborative search companion system, CoFeed, that collects user search queries and accesses feedback to build user- and document-centric profiling information. Over time, the system constructs ranked collections of elements that maintain the required information diversity and enhance the user search experience by presenting additional results tailored to the user interest space. This collaborative search companion requires a supporting architecture adapted to large user populations generating high request loads. To that end, it integrates mechanisms for ensuring scalability and load balancing of the service under varying loads and user interest distributions. Experiments with a deployed prototype highlight the efficiency of the system by analyzing improvement in search relevance, computational cost, scalability and load balance.