New Sequential Recommendation Model Blends Global and Intent-Aware Representations

Researchers have introduced a new method for sequential recommendation, the task of predicting what a user will want next based on the order of their past interactions with items. The approach, called Collaborative Enhancement of Global and Disentangled Representations for Sequential Recommendation (CEGDSR), is described in a paper published in Knowledge and Information Systems by Li Liu, Yiliang Lai and Chen Lin.

The starting point for the work is a common weakness in existing systems. Most current methods rely on item-level self-attention mechanisms that model transitions between items in a user’s history. According to the authors, this design is susceptible to noise, because users’ interactions are driven by complex and diverse intentions, and the resulting attention distribution can become inaccurate. Capturing users’ real intentions from historical behavior sequences remains an essential task in sequential recommendation.

CEGDSR addresses this by combining two kinds of representations through what the authors call collaborative enhancement. First, the model learns disentangled intent-aware user representations by projecting item representations into multiple intent subspaces, allowing different underlying intentions to be separated out. In parallel, an attention denoising module is devised to improve the global representation, which summarizes the user’s overall behavior pattern.

The two streams are then brought together. An attention fusion module fuses intent-level attention information into the global attention weights, so that the signals uncovered in the intent subspaces inform the broader representation. Finally, the method performs user relationship alignment between the enhanced global representation and the intent-disentangled representation, promoting mutual enhancement between the two and, in the authors’ words, implementing better fusion of global and disentangled intent-aware representations.

The paper reports extensive experiments on four real-world datasets, in which CEGDSR outperformed several strong baselines. The full article is available behind a subscription paywall, and the published abstract does not include the specific datasets, baseline methods or performance figures, so the magnitude of the improvement cannot be assessed from the publicly available material. The work appeared in Volume 68 of the journal as article number 294, with publication dated 23 October 2026.

Subject of Research: A machine learning method for sequential recommendation that fuses global and disentangled intent-aware user representations.

Article Title: Collaborative enhancement of global and disentangled representations for sequential recommendation

Article References: Liu, L., Lai, Y., & Lin, C. (2026). Collaborative enhancement of global and disentangled representations for sequential recommendation. Knowledge and Information Systems, 68(1), Article 294. https://doi.org/10.1007/s10115-026-02921-z

Image Credits: AI Generated

DOI: 10.1007/s10115-026-02921-z

Keywords: sequential recommendation, recommender systems, self-attention, intent disentanglement, attention denoising, representation learning, user behavior modeling, machine learning, Knowledge and Information Systems, Collaborative, enhancement, global