This paper describes a multiagent recommender approach based on the collaboration of multiple agents exchanging information stored in their local knowledge bases. A recommendation request is divided into sub-tasks handled by different agents, each one maintaining incomplete information that may be useful to compose a recommendation. Each agent has a Distributed Truth Maintenance component that helps to keep the integrity of its knowledge base. Further, we show a case study in the tourism domain where agents collaborate to recommend a travel package to the user. In order to help the coordination among the agents during the recommendation, the Distributed Constraint Optimization approach is applied in the search process.