Learning Similarity among Users for Personalized Session-Based
  Recommendation from hierarchical structure of User-Session-Item
Jisoo Cha Haemin Jeong Wooju Kim
The task of the session-based recommendation is to predict the next
interaction of the user based on the anonymized user's behavior pattern. And
personalized version of this system is a promising research field due to its
availability to deal with user information. However, there's a problem that the
user's preferences and historical sessions were not considered in the typical
session-based recommendation since it concentrates only on user-item
interaction. In addition, the existing personalized session-based
recommendation model has a limited capability in that it only considers the
preference of the current user without considering those of similar users. It
means there can be the loss of information included within the hierarchical
data structure of the user-session-item. To tackle with this problem, we
propose USP-SBR(abbr. of User Similarity Powered - Session Based Recommender).
To model global historical sessions of users, we propose UserGraph that has two
types of nodes - ItemNode and UserNode. We then connect the nodes with three
types of edges. The first type of edges connects ItemNode as chronological
order, and the second connects ItemNode to UserNode, and the last connects
UserNode to ItemNode. With these user embeddings, we propose additional
contrastive loss, that makes users with similar intention be close to each
other in the vector space. we apply graph neural network on these UserGraph and
update nodes. Experimental results on two real-world datasets demonstrate that
our method outperforms some state-of-the-art approaches.