  In this paper, we present a MLP-like architecture for sequential
recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token
communications. In designing Triangular Mixer, we simplify the cross-token
operation in MLP as the basic matrix multiplication, and drop the
lower-triangle neurons of the weight matrix to block the anti-chronological
order connections from future tokens. Accordingly, the information leakage
issue can be remedied and the prediction capability of MLP can be fully
excavated under the standard auto-regressive mode. Take a step further, the
mixer serially alternates two delicate MLPs with triangular shape, tagged as
global and local mixing, to separately capture the long range dependencies and
local patterns on fine-grained level, i.e., long and short-term preferences.
Empirical study on 12 datasets of different scales (50K\textasciitilde 10M
user-item interactions) from 4 benchmarks (Amazon, MovieLens, Tenrec and LBSN)
show that TriMLP consistently attains promising accuracy/efficiency trade-off,
where the average performance boost against several state-of-the-art baselines
achieves up to 14.88% with 8.65% less inference cost.
