short-paper

PKG: A Personal Knowledge Graph for Recommendation

Authors:

Yu Yang,

Jiangxu Lin,

Xiaolian Zhang,

Meng WangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3334 - 3338

https://doi.org/10.1145/3477495.3531671

Published: 07 July 2022 Publication History

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Abstract

Mobile internet users generate personal data on the devices all the time in this era. In this paper, we demonstrate a novel system for integrating the data of a user from different sources into a Personal Knowledge Graph, i.e., PKG. We show how a user's intention can be detected and how the personal data can be aligned and connected by the user behaviors. The constructed PKG allows the system makes reasonable and accurate recommendations for users by a "neural + symbolic'' approach across different services. Our system is shown in https://youtu.be/hWuo8KCDrto.

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Cited By

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Cui WWang DHan N(2024)Survey on Fake Information Generation, Dissemination and DetectionChinese Journal of Electronics10.23919/cje.2022.00.36233:3(573-583)Online publication date: May-2024
https://doi.org/10.23919/cje.2022.00.362
Liu LDu HZhang XGuo MWang HWang MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)A Question-Answering Assistant over Personal Knowledge GraphProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657665(2708-2712)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657665
Lee YYen AHuang HChen HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ConvLogRecaller: Real-Time Conversational Lifelog RecallerProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657659(2724-2728)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657659
Show More Cited By

Index Terms

PKG: A Personal Knowledge Graph for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

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the National Key Research and Development Program of China with Grant
the Fundamental Research Funds for the Central Universities
the National Natural Science Foundation of China with Grant

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SIGIR '22

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SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cui WWang DHan N(2024)Survey on Fake Information Generation, Dissemination and DetectionChinese Journal of Electronics10.23919/cje.2022.00.36233:3(573-583)Online publication date: May-2024
https://doi.org/10.23919/cje.2022.00.362
Liu LDu HZhang XGuo MWang HWang MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)A Question-Answering Assistant over Personal Knowledge GraphProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657665(2708-2712)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657665
Lee YYen AHuang HChen HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ConvLogRecaller: Real-Time Conversational Lifelog RecallerProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657659(2724-2728)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657659
Jiang XXu CShen YWang YSu FShi ZSun FLi ZGuo JShen HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph DatasetsProceedings of the ACM Web Conference 202410.1145/3589334.3645720(2325-2336)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645720
Yang ZLi L(2024)Knowledge graph-based recommendation with knowledge noise reduction and data augmentationApplied Intelligence10.1007/s10489-024-05657-x54:21(10333-10359)Online publication date: 13-Aug-2024
https://doi.org/10.1007/s10489-024-05657-x
Hu X(2024)Functional Knowledge Graph Towards Knowledge Application and Data Management for General UsersWeb and Big Data10.1007/978-981-97-7235-3_19(287-299)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_19
Fahl W(2024)GraphWiseLearn: Personalized Learning Through Semantified TEL, Leveraging QA-Enhanced LLM-Generated ContentThe Semantic Web: ESWC 2024 Satellite Events10.1007/978-3-031-78955-7_8(74-83)Online publication date: 26-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-78955-7_8
Liu DWu YLi WZhang XWang HYang QMing Z(2023)Pairwise Intent Graph Embedding Learning for Context-Aware RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608815(588-598)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608815
Zhu QZhang HHe QDou Z(2023)Query-Aware Explainable Product Search With Reinforcement Knowledge Graph ReasoningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329733136:3(1260-1273)Online publication date: 24-Jul-2023
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Liang YLiu LHuangfu LWang ZGuo B(2023)DeepApp: characterizing dynamic user interests for mobile application recommendationWorld Wide Web10.1007/s11280-023-01161-326:5(2623-2645)Online publication date: 2-May-2023
https://dl.acm.org/doi/10.1007/s11280-023-01161-3
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Recommendations

Jointing Knowledge Graph and Neural Network for Top-N Recommendation

Personalized recommendation system based on knowledge embedding and historical behavior

Research Summary of Recommendation System Based on Knowledge Graph

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