abstract: Session-based recommendation is critical in modern rec- ommender systems, which aims to predict the next interested item given anonymous behavior sequences of users. While prior works have made efforts to addressing the session-based recommendation problem, two significant limitations exist: i) They ignore the fact that items may be correlated with other across different session units; ii) existing solutions are also limited in their assumption of rigidly ordered pattern over intra-session item transition, which may not be true in practice. To address these above limitations, we propose a Local-Global Session-based Recommendation framework–LGSR which generalizes the modeling of behavior dynamics from two per- spectives: we first design a cross-session item dependency encoder to learn the inter-session item relation structures from a global perspec- tive. Additionally, a dual-stage attentive aggregation module is devel- oped to capture local item transition dynamics, without the restriction of rigid sequential process for jointly modeling user’s current inter- est and intra-session purpose. With the exploration of both complex intra- and inter-session interest transitional regularities, our LGSR model enables the representation learning of user behavior dynam- ics via jointly mapping local and global signals into the same latent space. The experimental results on two real-world datasets demon- strate the superiority of the proposed LGSR framework over state- of-the-art methods.
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