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World Wide Web

DIRS-KG: a KG-enhanced interactive recommender system based on deep reinforcement learning

  • 01-04-2023
Published in:
Authors
Ronghua Lin
Feiyi Tang
Chaobo He
Zhengyang Wu
Chengzhe Yuan
Yong Tang
Published in
World Wide Web | Issue 5/2023

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Abstract

The article introduces DIRS-KG, a deep reinforcement learning-based interactive recommender system that leverages knowledge graphs to enhance recommendation accuracy. It addresses the limitations of conventional systems by modeling both user immediate and long-term feedback. The system uses user top-l latest interactions as the state and employs Bi-LSTM layers to learn dynamic preferences. Additionally, it integrates knowledge graph embeddings to enrich item representations, improving recommendation performance. Extensive experiments on real-world datasets demonstrate the superiority of DIRS-KG over existing baseline methods.
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previous article A performant and incremental algorithm for knowledge graph entity typing
next article A novel cross-network node pair embedding methodology for anchor link prediction
Title
DIRS-KG: a KG-enhanced interactive recommender system based on deep reinforcement learning
Authors
Ronghua Lin
Feiyi Tang
Chaobo He
Zhengyang Wu
Chengzhe Yuan
Yong Tang
Publication date
01-04-2023
Publisher
Springer US
Published in
World Wide Web / Issue 5/2023
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-022-01135-x
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Metacognition-driven user-to-project recommendation for online education services

Quantifying controversy from stance, sentiment, offensiveness and sarcasm: a fine-grained controversy intensity measurement framework on a Chinese dataset

A novel cross-network node pair embedding methodology for anchor link prediction

Editor’s note: Special issue on knowledge-graph-enabled methods and applications for the future web

  • Editorial

Integrating fine-grained attention into multi-task learning for knowledge tracing

Efficient maintenance of highway cover labelling for distance queries on large dynamic graphs

  • Open Access

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