  Recommender systems, which merely leverage user-item interactions for user
preference prediction (such as the collaborative filtering-based ones), often
face dramatic performance degradation when the interactions of users or items
are insufficient. In recent years, various types of side information have been
explored to alleviate this problem. Among them, knowledge graph (KG) has
attracted extensive research interests as it can encode users/items and their
associated attributes in the graph structure to preserve the relation
information. In contrast, less attention has been paid to the item-item
co-occurrence information (i.e., \textit{co-view}), which contains rich
item-item similarity information. It provides information from a perspective
different from the user/item-attribute graph and is also valuable for the CF
recommendation models. In this work, we make an effort to study the potential
of integrating both types of side information (i.e., KG and item-item
co-occurrence data) for recommendation. To achieve the goal, we propose a
unified graph-based recommendation model (UGRec), which integrates the
traditional directed relations in KG and the undirected item-item co-occurrence
relations simultaneously. In particular, for a directed relation, we transform
the head and tail entities into the corresponding relation space to model their
relation; and for an undirected co-occurrence relation, we project head and
tail entities into a unique hyperplane in the entity space to minimize their
distance. In addition, a head-tail relation-aware attentive mechanism is
designed for fine-grained relation modeling. Extensive experiments have been
conducted on several publicly accessible datasets to evaluate the proposed
model. Results show that our model outperforms several previous
state-of-the-art methods and demonstrate the effectiveness of our UGRec model.
