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Collaborative recommender systems use various types of information to help customers find products of personalized interest. To increase the usefulness of collaborative recommender systems in certain circumstances, it could be desirable to merge recommender system databases between companies, thus expanding the data pool. This can lead to privacy disclosure hazards during the merging process. This paper addresses how to avoid privacy disclosure in collaborative recommender systems by comparing with major cryptology approaches and constructing a more efficient privacy-preserving collaborative recommender system based on the scalar product protocol.