SessionRec: Next Session Prediction Paradigm For Generative Sequential Recommendation
This addresses the problem of inefficient and unrealistic modeling in sequential recommendation for industrial-scale systems, though it appears incremental as it builds on existing paradigms with architectural improvements.
The paper tackles the misalignment between conventional next-item prediction and real-world recommendation scenarios by introducing SessionRec, a next-session prediction paradigm for generative sequential recommendation. It demonstrates effectiveness through experiments on public datasets and an online A/B test in Meituan App, showing improved ranking and computational efficiency.
We introduce SessionRec, a novel next-session prediction paradigm (NSPP) for generative sequential recommendation, addressing the fundamental misalignment between conventional next-item prediction paradigm (NIPP) and real-world recommendation scenarios. Unlike NIPP's item-level autoregressive generation that contradicts actual session-based user interactions, our framework introduces a session-aware representation learning through hierarchical sequence aggregation (intra/inter-session), reducing attention computation complexity while enabling implicit modeling of massive negative interactions, and a session-based prediction objective that better captures users' diverse interests through multi-item recommendation in next sessions. Moreover, we found that incorporating a rank loss for items within the session under the next session prediction paradigm can significantly improve the ranking effectiveness of generative sequence recommendation models. We also verified that SessionRec exhibits clear power-law scaling laws similar to those observed in LLMs. Extensive experiments conducted on public datasets and online A/B test in Meituan App demonstrate the effectiveness of SessionRec. The proposed paradigm establishes new foundations for developing industrial-scale generative recommendation systems through its model-agnostic architecture and computational efficiency.