HTP: Exploiting Holistic Temporal Patterns for Sequential Recommendation

TL;DR

This paper introduces HTP, a neural network that leverages three types of temporal patterns—absolute time, relative item intervals, and relative recommendation intervals—for improved sequential recommendation accuracy.

Contribution

It is the first to explore item-oriented absolute time patterns and integrates all three temporal patterns into a unified model, addressing their complex correlations.

Findings

HTP outperforms state-of-the-art models on benchmark datasets.

Incorporating all three temporal patterns yields significant accuracy improvements.

The model effectively captures subtle temporal correlations.

Abstract

Sequential recommender systems have demonstrated a huge success for next-item recommendation by explicitly exploiting the temporal order of users' historical interactions. In practice, user interactions contain more useful temporal information beyond order, as shown by some pioneering studies. In this paper, we systematically investigate various temporal information for sequential recommendation and identify three types of advantageous temporal patterns beyond order, including absolute time information, relative item time intervals and relative recommendation time intervals. We are the first to explore item-oriented absolute time patterns. While existing models consider only one or two of these three patterns, we propose a novel holistic temporal pattern based neural network, named HTP, to fully leverage all these three patterns. In particular, we introduce novel components to address the subtle correlations between relative item time intervals and relative recommendation time intervals, which render a major technical challenge. Extensive experiments on three real-world benchmark datasets show that our HTP model consistently and substantially outperforms many state-of-the-art models. Our code is publically available at https://github.com/623851394/HTP/tree/main/HTP-main