  The item cold-start problem seriously limits the recommendation performance
of Collaborative Filtering (CF) methods when new items have either none or very
little interactions. To solve this issue, many modern Internet applications
propose to predict a new item's interaction from the possessing contents.
However, it is difficult to design and learn a map between the item's
interaction history and the corresponding contents. In this paper, we apply the
Wasserstein distance to address the item cold-start problem. Given item content
information, we can calculate the similarity between the interacted items and
cold-start ones, so that a user's preference on cold-start items can be
inferred by minimizing the Wasserstein distance between the distributions over
these two types of items. We further adopt the idea of CF and propose
Wasserstein CF (WCF) to improve the recommendation performance on cold-start
items. Experimental results demonstrate the superiority of WCF over
state-of-the-art approaches.
