  The common item-based collaborative filtering framework becomes a typical
recommendation method when equipped with a certain item-to-item similarity
measurement. On one hand, we realize that a well-designed similarity
measurement is the key to providing satisfactory recommendation services. On
the other hand, similarity measurements designed for sequential recommendation
are rarely studied by the recommender systems community. Hence in this paper,
we focus on devising a novel similarity measurement called position-aware
similarity (PAS) for sequential recommendation. The proposed PAS is, to our
knowledge, the first count-based similarity measurement that concurrently
captures the sequential patterns from the historical user behavior data and
from the item position information within the input sequences. We conduct
extensive empirical studies on four public datasets, in which our proposed
PAS-based method exhibits competitive performance even compared to the
state-of-the-art sequential recommendation methods, including a very recent
similarity-based method and two GNN-based methods.
