PAS: A Position-Aware Similarity Measurement for Sequential Recommendation

TL;DR

This paper introduces PAS, a novel position-aware similarity measure for sequential recommendation that captures sequential patterns and item positions, demonstrating competitive performance on public datasets.

Contribution

The paper proposes PAS, the first count-based similarity measure that incorporates both sequential patterns and item position information for recommendation.

Findings

PAS achieves competitive results compared to state-of-the-art methods.

Extensive experiments validate the effectiveness of PAS across datasets.

PAS outperforms traditional similarity measures in sequential recommendation tasks.

Abstract

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.