The major challenges of multimedia retrieval are the difficulty of generating semantic indexes, as well as the incapability of identifying personalized user interests. This paper attempts to address both problems by suggesting a collaborative yet personalized approach for Web-based multimedia retrieval, which employs a synergy between the relevance feedback technique from the information retrieval community, and the user profiling technique from the information filtering community. Specifically, a "common profile" is established to represent the common knowledge on the semantics of multimedia data, which allow a user to "learn from others" in the retrieval process. On the other hand, for each user a "user profile" is constructed to characterize his/her personal views, which allow a user to "learn from own history". Both types of profiles can be learned and updated incrementally from user feedback. By using an integrated retrieval algorithm based on profiles, this approach strikes the balance between exploiting the common knowledge of most users and catering for the personalized interest of a particular user. The results of some preliminary experiments have demonstrated the effectiveness of the proposed approach.