We have developed Papits, a research support system, that shares research information, such as PDF files of research papers, in computers on networks and classifies the information into research types. Papits users can share various research information and survey the corpora of their particular fields. To develop Papits, we need to design a mechanism to identify a user’s interest. Also, when constructing an effective paper recommendation system, it is important to carefully create user’s models. We propose a method to construct user’s models using the scale-free network. The scale-free network has vertices and edges, and ensures growth by ‘preference attachments’. Our method applies a paper viewing history to construct a scale-free network based on the word co-occurrence. A constructed network consists of vertices that represent words, and edges that represent the word co-occurrence. In our method, a paper is added to the network as indicated by a user’s paper viewing history. Additionally we define the ‘topic weight’. By using two elements; the topic frequency and the topic recency, we calculate the topic weight. By using the word cooccurrence in a database, we measure the topic frequency. Also, by using the Jaccard coefficient, we measure the topic recency. Our result indicates that our method can effectively recommend documents for Papits users.