  Recommender systems are fundamental information filtering techniques to
recommend content or items that meet users' personalities and potential needs.
As a crucial solution to address the difficulty of user identification and
unavailability of historical information, session-based recommender systems
provide recommendation services that only rely on users' behaviors in the
current session. However, most existing studies are not well-designed for
modeling heterogeneous user behaviors and capturing the relationships between
them in practical scenarios. To fill this gap, in this paper, we propose a
novel graph-based method, namely Heterogeneous Information Crossing on Graphs
(HICG). HICG utilizes multiple types of user behaviors in the sessions to
construct heterogeneous graphs, and captures users' current interests with
their long-term preferences by effectively crossing the heterogeneous
information on the graphs. In addition, we also propose an enhanced version,
named HICG-CL, which incorporates contrastive learning (CL) technique to
enhance item representation ability. By utilizing the item co-occurrence
relationships across different sessions, HICG-CL improves the recommendation
performance of HICG. We conduct extensive experiments on three real-world
recommendation datasets, and the results verify that (i) HICG achieves the
state-of-the-art performance by utilizing multiple types of behaviors on the
heterogeneous graph. (ii) HICG-CL further significantly improves the
recommendation performance of HICG by the proposed contrastive learning module.
