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PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems

Authors:

Sangkeun Lee,

Sungchan Park,

Minsuk Kahng, and

Sang-goo LeeAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

Pages 1637 - 1641

https://doi.org/10.1145/2396761.2398488

Published: 29 October 2012 Publication History

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Abstract

In this paper, we present a novel random-walk based node ranking measure, PathRank, which is defined on a heterogeneous graph by extending the Personalized PageRank algorithm. Not only can our proposed measure exploit the semantics behind the different types of nodes and edges in a heterogeneous graph, but also it can emulate various recommendation semantics such as collaborative filtering, content-based filtering, and their combinations. The experimental results show that PathRank can produce more various and effective recommendation results compared to existing approaches.

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Yue HLiu HChen J(2022) A Gospel for MOBA Game: Ranking-Preserved Hero Change Prediction in Dota 2 IEEE Transactions on Games10.1109/TG.2021.312358314:2(191-201)Online publication date: Jun-2022
https://doi.org/10.1109/TG.2021.3123583
Ficel HHaddad MBaazaoui Zghal H(2021)A graph-based recommendation approach for highly interactive platformsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115555185:COnline publication date: 15-Dec-2021
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Index Terms

PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2012

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October 29 - November 2, 2012

Hawaii, Maui, USA

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https://doi.org/10.1007/s11704-022-2457-y
Yue HLiu HChen J(2022) A Gospel for MOBA Game: Ranking-Preserved Hero Change Prediction in Dota 2 IEEE Transactions on Games10.1109/TG.2021.312358314:2(191-201)Online publication date: Jun-2022
https://doi.org/10.1109/TG.2021.3123583
Ficel HHaddad MBaazaoui Zghal H(2021)A graph-based recommendation approach for highly interactive platformsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115555185:COnline publication date: 15-Dec-2021
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Jung JJin WPark HKang U(2021)Accurate relational reasoning in edge-labeled graphs by multi-labeled random walk with restartWorld Wide Web10.1007/s11280-020-00817-824:4(1369-1393)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s11280-020-00817-8
Wang HSong XLiu YChen CZhou B(2021)A Meta-path Based Graph Convolutional Network with Multi-scale Semantic Extractions for Heterogeneous Event ClassificationAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-75762-5_37(459-471)Online publication date: 9-May-2021
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Ristoski PRosati JDi Noia TDe Leone RPaulheim H(2019)RDF2Vec: RDF graph embeddings and their applicationsSemantic Web10.3233/SW-18031710:4(721-752)Online publication date: 23-May-2019
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Lopes RAssunção RSantos R(2019)Graph-based Recommendation Meets Bayes and Similarity MeasuresACM Transactions on Intelligent Systems and Technology10.1145/335688211:1(1-26)Online publication date: 14-Dec-2019
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PathRank: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems

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