  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.
