Recommendation systems are becoming important with the increased availability of online
services. A typical approach used in recommendations is collaborative filtering. However,
because it largely relies on external relations, such as items-to-items or users-to-users,
problems occur when the relations are biased or insufficient. Focusing on that limitation, we
here suggest a new method, item-network-based collaborative filtering, which recommends
items through four steps. First, the system constructs item networks based on users' item …