Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence. Although effective, we reveal two inherent limitations:(1) it may differ from human behavior in that a user could imagine an oracle item in mind and select potential items matching the oracle; and (2) the classification is limited in the candidate pool with noisy or easy supervision from negative samples, which dilutes the preference signals towards the oracle item. Yet, generating the oracle item from the historical interaction sequence is mostly unexplored. To bridge the gap, we reshape sequential recommendation as a learning-to-generate paradigm, which is achieved via a guided diffusion model, termed DreamRec.Specifically, for a sequence of historical items, it applies a Transformer encoder to create guidance representations. Noising target items explores the underlying distribution of item space; then, with the guidance of historical interactions, the denoising process generates an oracle item to recover the positive item, so as to cast off negative sampling and depict the true preference of the user directly. We evaluate the effectiveness of DreamRec through extensive experiments and comparisons with existing methods. Codes and data are open-sourced at https://github.com/YangZhengyi98/DreamRec.

翻译：序列推荐旨在根据用户之前交互的项目序列，推荐与用户兴趣匹配的下一个项目。审视先前研究，我们可以总结出一种常见的“学习-分类”范式——给定一个正样本，推荐模型通过负采样添加负样本，并基于用户的历史交互序列学习分类用户是否偏好它们。尽管有效，我们揭示了两个固有局限：（1）这可能与人类行为不同，因为用户可能在心中构想出一个理想项目，并选择与理想匹配的潜在项目；（2）分类局限于候选池中，且来自负样本的噪声或简单监督会稀释对理想项目的偏好信号。然而，从历史交互序列生成理想项目这一方向大多未被探索。为弥补这一差距，我们将序列推荐重塑为一种“学习-生成”范式，通过一种被称为DreamRec的引导扩散模型实现。具体而言，对于历史项目序列，它应用Transformer编码器创建引导表示。对目标项目加噪以探索项目空间的潜在分布；然后，在历史交互的引导下，去噪过程生成一个理想项目以恢复正样本，从而摆脱负采样并直接描绘用户的真实偏好。我们通过大量实验及与现有方法的比较，评估了DreamRec的有效性。代码和数据已在https://github.com/YangZhengyi98/DreamRec开源。