COLANet: Cross-Domain Recommender Systems with Latent Overlapping Items on Graph Neural Networks

Pongsakorn Jirachanchaisiri, Saranya Maneeroj, Atsuhiro Takasu

Abstract: Cross-domain recommender systems (CDRSs) enhance recommendations by transferring knowledge of overlapping users across two domains. Deep canonical correlation analysis (DCCA) shows promising results in CDRSs by maximizing correlations between representations of overlapping users, enabling cross-domain knowledge transfer that depends on the degree of relationship between domains. As a result, DCCA selectively shares only relevant knowledge, alleviating the problem of noisy representation found in traditional CDRSs, where they transfer knowledge regardless of the correlation strength between domains. Although DCCA is used for user transfer, item transfer, referring to the transfer of explicit knowledge of the same items between domains, is impossible due to the absence of overlapping items to facilitate direct knowledge transfer. Meanwhile, graph neural networks (GNNs) embed users and items from separate user and item graphs in each domain. Therefore, better representations are obtained from captured complex relationships and collaborative signals. To construct graphs of overlapping items, latent linkages among items between domains could be discovered by the neural topic model (NTM), forming new graphs representing the latent relationships. Therefore, COLANet, a GNN-based CDRS, is proposed to solve the DCCA limitation on item transfer by proposing the extraction of item representations that do not exist in another domain using latent characteristics. First, user-user graphs are constructed using user similarity, and the item-topic graph is constructed using latent topics learned from item descriptions with NTM. Hence, user and item graphs of each domain are constructed separately, preventing domain relationship misalignment. Second, these graphs are fed to GNN to obtain user and item representations. Third, these representations are fed to DCCA to transfer knowledge between user-user and item-item. Finally, correlated user and item representations of each domain are used to predict ratings. The experiments demonstrate that COLANet outperforms the baselines across four pairs of domains, including both similar and different domains.

External IDs:dblp:journals/tkdd/JirachanchaisiriMT25