Sequential Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge and users’ historical behaviors for the next-item prediction. In this paper, we focus on the cross-domain sequential recommendation problem. This commonly exist problem is rather challenging from two perspectives, i.e., the implicit user historical rating sequences are difficult in modeling and the users/items on different domains are mostly non-overlapped. Most previous sequential CDR approaches cannot solve the cross-domain sequential recommendation problem well, since (1) they cannot sufficiently depict the users’ actual preferences, (2) they cannot leverage and transfer useful knowledge across domains. To tackle the above issues, we propose joint Internal multi-interest exploration and External domain alignment for cross domain Sequential Recommendation model (IESRec). IESRec includes two main modules, i.e., internal multi-interest exploration module and external domain alignment module. To reflect the users’ diverse characteristics with multi-interests evolution, we first propose internal temporal optimal transport method in the internal multi-interest exploration module. We further propose external alignment optimal transport method in the external domain alignment module to reduce domain discrepancy for the item embeddings. Our empirical studies on Amazon datasets demonstrate that IESRec significantly outperforms the state-of-the-art models.

Session-based Recommendation aims at predicting the next interacted item based on short anonymous behavior sessions. However, existing solutions neglect to model two inherent properties of sequential representing distributions, i.e., hierarchy structures resulted from item popularity and collaborations existing in both intra- and inter-session. Tackling with these two factors at the same time is challenging. On the one hand, traditional Euclidean space utilized in previous studies fails to capture hierarchy structures due to a restricted representation ability. On the other hand, the intuitive apply of hyperbolic geometry could extract hierarchical patterns but more emphasis on degree distribution weakens intra- and inter-session collaborations. To address the challenges, we propose a Hierarchy-Aware Dual Clustering Graph Network (HADCG) model for session-based recommendation. Towards the first challenge, we design the hierarchy-aware graph modeling module which converts sessions into hyperbolic session graphs, adopting hyperbolic geometry in propagation and attention mechanism so as to integrate chronological and hierarchical information. As for the second challenge, we introduce the deep dual clustering module which develops a two-level clustering strategy, i.e., information regularizer for intra-session clustering and contrastive learner for inter-session clustering, to enhance hyperbolic representation learning from collaborative perspectives and further promote recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed HADCG.