Keywords: Sequential Recommendation, Recommendation System, Generative Model, Diffusion Model
Abstract: 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.
Submission Number: 6342
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