Abstract: Multi-interest sequential recommendation models extract multiple user interests through a dynamic routing mechanism to achieve personalized recommendations. However, during the interest extraction phase, improper item–interest routing weight allocation may cause multiple interest representations to become excessively similar, leading to the multi-interest collapse issue. In the prediction phase, neglecting users’ preference intensity across different interests may assign comparable or even greater influence to low-preference interests than to high-preference ones, resulting in the multi-interest preference weight imbalance issue. To address these issues, we propose a multi-interest sequential recommendation model based on disentangled feature representation and adaptive weight fusion (DMIAFRec). First, the model partitions items according to their co-occurrence relationships, grouping frequently co-occurring items with complementary semantic characteristics into the same interest group. This partition serves as a structural guidance mechanism for routing weight allocation, encouraging each group to focus on relatively independent user interests, thereby achieving disentangled multi-interest representations and preventing excessive convergence among interest vectors. Furthermore, a time-decay mechanism and a multi-interest attention fusion strategy are introduced to adaptively assign weights according to users’ preference intensity for each interest. The weighted aggregation of multiple interest representations produces a unified user preference representation that reflects heterogeneous interest importance, thus enhancing personalized recommendation performance. Experimental results on three public datasets show that, compared with the best baseline model, the proposed model achieves average improvements of 6.2%, 4.98%, and 4.07% in R@20, NDCG@20, and HR@20, respectively, on the Retail Rocket, Gowalla, and Books datasets, demonstrating its effectiveness in improving recommendation performance and addressing the aforementioned issues.

摘要： 多兴趣序列推荐算法通过动态路由机制提取用户多种兴趣以实现个性化推荐。然而，在多兴趣提取阶段，不合理的项目-兴趣路由权重分配会导致多个兴趣表示过于相似，从而引发多兴趣崩溃问题；在预测阶段，未考虑用户对不同兴趣的偏好程度，赋予了偏好程度较低的兴趣与偏好程度较高的兴趣相当甚至更强的推荐影响力，即用户多兴趣偏好权重失衡问题。针对上述问题，提出了基于解耦特征表示与自适应权重融合的多兴趣序列推荐模型(DMIAFRec)。首先，模型基于项目间的共现关系对项目进行划分，将频繁共现且具有互补语义关系的项目归入同一兴趣组，并以此作为路由权重分配的引导机制。该设计促使每个组的项目聚焦于相互独立的特定用户兴趣，从而实现多兴趣表示的解耦，避免多个兴趣表示过度趋同。此外，引入时间衰减机制与多兴趣注意力融合机制，根据用户对各兴趣的偏好程度自适应地分配权重，对多兴趣表示进行加权聚合得到综合了各兴趣偏好权重的用户偏好表示，从而提升个性化推荐效果。实验结果表明，该模型相较于最优基线模型相比，R@20、NDCG@20和HR@20指标在Retail Rocket、Gowalla和Books数据集上的平均提升了6.2%、4.98%和4.07%，证明了所提模型在提高推荐性能的有效性并能够有效地解决上述问题。