Knowledge-enhanced Session-based Recommendation with Temporal Transformer

TL;DR

This paper introduces KSTT, a novel session-based recommendation framework that leverages item knowledge graphs and temporal information via a temporal transformer to improve recommendation accuracy.

Contribution

The paper proposes integrating knowledge graphs and time interval embeddings into a transformer-based model for enhanced session-based recommendations.

Findings

Outperforms state-of-the-art baselines on four datasets.

Effectively incorporates item knowledge and temporal patterns.

Improves recommendation accuracy significantly.

Abstract

Recent research has achieved impressive progress in the session-based recommendation. However, information such as item knowledge and click time interval, which could be potentially utilized to improve the performance, remains largely unexploited. In this paper, we propose a framework called Knowledge-enhanced Session-based Recommendation with Temporal Transformer (KSTT) to incorporate such information when learning the item and session embeddings. Specifically, a knowledge graph, which models contexts among items within a session and their corresponding attributes, is proposed to obtain item embeddings through graph representation learning. We introduce time interval embedding to represent the time pattern between the item that needs to be predicted and historical click, and use it to replace the position embedding in the original transformer (called temporal transformer). The item embeddings in a session are passed through the temporal transformer network to get the session embedding, based on which the final recommendation is made. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on four benchmark datasets.