  Recently, electronic commerce (EC) websites have been unable to provide an
identification number (user ID) for each transaction data entry because of
privacy issues. Because most recommendation methods assume that all data are
assigned a user ID, they cannot be applied to the data without user IDs.
Recently, session-based recommendation (SBR) based on session information,
which is short-term behavioral information of users, has been studied. A
general SBR uses only information about the item of interest to make a
recommendation (e.g., item ID for an EC site). Particularly in the case of EC
sites, the data recorded include the name of the item being purchased, the
price of the item, the category hierarchy, and the gender and region of the
user. In this study, we define a pseudo--session for the purchase history data
of an EC site without user IDs and session IDs. Finally, we propose an SBR with
a co-guided heterogeneous hypergraph and globalgraph network plus, called
CoHHGN+. The results show that our CoHHGN+ can recommend items with higher
performance than other methods.
