A framework is described for a peer-to-peer information exchange system, and a collaborative information retrieval (IR) scheme for the system is proposed. The aims of the system include smooth knowledge and information management to activate organizations or communities. Conventional server-centric systems are weak because they create information-providing bottlenecks. Accordingly, the proposed framework targets the collaborative inter-working of personal repositories that accumulate per-user information, and accept and service requests. Issues concerning the framework are addressed. One issue is the retrieval of information from another's personal repository; the retrieval criteria of a system are tightly personalized for its user. The system is assumed to employ a vector space model with a concept-base as its IR mechanism. The vector space on one system is very different from that on another system. Another issue is the automated control of the information-providing criteria. This paper presents solutions to the first problem. To achieve IR that provides satisfactory results to a user requiring information from another's personal repository, we need vector space equalization to compensate for the differences in the vector spaces of the personal repositories. The paper presents a vector space equalization scheme, the automated relevance feedback scheme, that compensates the differences in the vector spaces of the personal repositories. We implement the scheme as a system and evaluate its performance using documents on the Internet.