Abstract
Sequential recommendation systems have become
a research hotpot recently to suggest users with the
next item of interest (to interact with). However,
existing approaches suffer from two limitations: (1)
The representation of an item is relatively static and
fixed for all users. We argue that even a same item
should be represented distinctively with respect to
different users and time steps. (2) The generation
of a prediction for a user over an item is computed
in a single scale (e.g., by their inner product), ignoring the nature of multi-scale user preferences.
To resolve these issues, in this paper we propose
two enhancing building blocks for sequential recommendation. Specifically, we devise a Dynamic
Item Block (DIB) to learn dynamic item representation by aggregating the embeddings of those who
rated the same item before that time step. Then,
we come up with a Prediction Enhancing Block
(PEB) to project user representation into multiple scales, based on which many predictions can
be made and attentively aggregated for enhanced
learning. Each prediction is generated by a softmax
over a sampled itemset rather than the whole item
space for efficiency. We conduct a series of experiments on four real datasets, and show that even a
basic model can be greatly enhanced with the involvement of DIB and PEB in terms of ranking
accuracy. The code and datasets can be obtained
from https://github.com/ouououououou/DIB-PEBSequential-RS