Sequential recommendation (SR) models the dynamic user preferences and generates the next-item prediction as the affinity between the sequence and items, in a joint latent space with low dimensions (i.e., the sequence and item embedding space). Both sequence and item representations suffer from the representation degeneration issue due to the user/item long-tail distributions, where tail users/ items are indistinguishably distributed as a narrow cone in the latent space. We argue that the representation degeneration issue is the root cause of insufficient recommendation diversity in existing SR methods, impairing the user potential exploration and further worsening the echo chamber issue. In this work, we first disclose the connection between the representation degeneration and recommendation diversity, in which severer representation degeneration indicates lower recommendation diversity. We then propose a novel Singular sPectrum sMoothing regularization for Recommendation (SPMRec), which acts as a controllable surrogate to alleviate the degeneration and achieve the balance between recommendation diversity and performance. The proposed smoothing regularization alleviates the degeneration by maximizing the area under the singular value curve, which is also the diversity surrogate. We conduct experiments on four benchmark datasets to demonstrate the superiority of SPMRec, and show that the proposed singular spectrum smoothing can control the balance of recommendation performance and diversity simultaneously.

翻译：序列推荐（SR）通过建模动态用户偏好，在低维联合潜在空间（即序列与物品嵌入空间）中，将序列与物品间的亲和度作为下一物品预测依据。由于用户/物品的长尾分布，序列与物品表征均面临表征退化问题，即尾部用户/物品在潜在空间中难以区分地分布为狭窄锥形区域。我们认为，表征退化问题是现有序列推荐方法中推荐多样性不足的根本原因，这不仅损害了用户潜在探索的可能性，还进一步加剧了信息茧房效应。本研究首先揭示了表征退化与推荐多样性之间的内在关联：更严重的表征退化将导致更低的推荐多样性。继而提出一种新颖的基于奇异谱平滑正则化的推荐方法（SPMRec），该方法作为可控代理机制以缓解表征退化，实现推荐多样性与性能的平衡。所提出的平滑正则化通过最大化奇异值曲线下面积来缓解退化问题，该面积同时可作为多样性代理指标。我们在四个基准数据集上进行了实验验证，结果表明SPMRec具有显著优越性，且所提出的奇异谱平滑方法能够同步调控推荐性能与多样性的平衡关系。