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This paper aims to examine whether users' watching networks can improve collaborative filtering-based recommendations (CF). Watching networks are established by users upon their perceived usefulness or interests about other users' information collections. The networks do not require mutual agreement between a watching party and a watched party. The typical example of this network is 'following' in Twitter, 'watching' on CiteULike, or 'contacts' on Flickr. Once a user declares that 'I want to watch user A', the user A's information collection is displayed to the watching user, continuously. It can beinterpreted to mean that a watching user found some shared interests in user A's collection and want to refer to it in future. The approaches explored in this paper take advantage of this watching network as a part of user's preferences for recommendations. To evaluate the potential of these approaches, we focus on a social tagging system, CiteULike. Our data shows that in this context, a hybrid recommendation approachthat fusesCF and watching network-based recommendations outperforms both CF and network-based recommendations.