Knowledge-enhanced Session-based Recommendation with Temporal Transformer
Rongzhi Zhang, Yulong Gu, Xiaoyu Shen, Hui Su

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRecommender Systems and Techniques · Machine Learning in Healthcare · Topic Modeling
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Dense Connections · Position-Wise Feed-Forward Layer · Residual Connection · Layer Normalization · Dropout · Label Smoothing · Byte Pair Encoding
