demonstration

HeteRecom: a semantic-based recommendation system in heterogeneous networks

Authors:

Gang Liu, and

Bai WangAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

Pages 1552 - 1555

https://doi.org/10.1145/2339530.2339778

Published: 12 August 2012 Publication History

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Abstract

Making accurate recommendations for users has become an important function of e-commerce system with the rapid growth of WWW. Conventional recommendation systems usually recommend similar objects, which are of the same type with the query object without exploring the semantics of different similarity measures. In this paper, we organize objects in the recommendation system as a heterogeneous network. Through employing a path-based relevance measure to evaluate the relatedness between any-typed objects and capture the subtle semantic containing in each path, we implement a prototype system (called HeteRecom) for semantic based recommendation. HeteRecom has the following unique properties: (1) It provides the semantic-based recommendation function according to the path specified by users. (2) It recommends the similar objects of the same type as well as related objects of different types. We demonstrate the effectiveness of our system with a real-world movie data set.

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Cited By

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Ammar KInoubli WZghal SMephu Nguifo E(2024)Graph Representation Learning for Recommendation Systems: A Short ReviewAdvances in Information Systems, Artificial Intelligence and Knowledge Management10.1007/978-3-031-51664-1_3(33-48)Online publication date: 20-Jan-2024
https://doi.org/10.1007/978-3-031-51664-1_3
Zhou YFang YLuo WYe Y(2023)Influential Community Search over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3594512.359453216:8(2047-2060)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.14778/3594512.3594532
Pham PNguyen LNguyen NKozma RVo B(2023)A hierarchical fused fuzzy deep neural network with heterogeneous network embedding for recommendationInformation Sciences10.1016/j.ins.2022.11.085620(105-124)Online publication date: Jan-2023
https://doi.org/10.1016/j.ins.2022.11.085
Show More Cited By

Index Terms

HeteRecom: a semantic-based recommendation system in heterogeneous networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

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Published In

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2012

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KDD '12

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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Cited By

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Ammar KInoubli WZghal SMephu Nguifo E(2024)Graph Representation Learning for Recommendation Systems: A Short ReviewAdvances in Information Systems, Artificial Intelligence and Knowledge Management10.1007/978-3-031-51664-1_3(33-48)Online publication date: 20-Jan-2024
https://doi.org/10.1007/978-3-031-51664-1_3
Zhou YFang YLuo WYe Y(2023)Influential Community Search over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3594512.359453216:8(2047-2060)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.14778/3594512.3594532
Pham PNguyen LNguyen NKozma RVo B(2023)A hierarchical fused fuzzy deep neural network with heterogeneous network embedding for recommendationInformation Sciences10.1016/j.ins.2022.11.085620(105-124)Online publication date: Jan-2023
https://doi.org/10.1016/j.ins.2022.11.085
Yao ZJi MXing TFu RLi SYin F(2023)Multi-component graph collaborative filtering using auxiliary information for TV program recommendationNeural Computing and Applications10.1007/s00521-023-08940-z35:30(22737-22754)Online publication date: 17-Aug-2023
https://doi.org/10.1007/s00521-023-08940-z
Lin HLiang GWu YWu BTian CWang W(2023)Open Source Software Supply Chain Recommendation Based on Heterogeneous Information NetworkBenchmarking, Measuring, and Optimizing10.1007/978-3-031-31180-2_5(70-86)Online publication date: 13-May-2023
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Jiang YFang YMa CCao XLi C(2022)Effective community search over large star-schema heterogeneous information networksProceedings of the VLDB Endowment10.14778/3551793.355179515:11(2307-2320)Online publication date: 1-Jul-2022
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Wang QZhu CZhang YZhong HZhong JSheng V(2022)Short Text Topic Learning Using Heterogeneous Information NetworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3147766(1-1)Online publication date: 2022
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Duong CNguyen TYin HWeidlich MMai TAberer KNguyen Q(2022)Efficient and Effective Multi-Modal Queries Through Heterogeneous Network EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.305287134:11(5307-5320)Online publication date: 1-Nov-2022
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Zhu ZChan TCheng RDo LHuang ZZhang H(2022)Effective and Efficient Discovery of Top-k Meta Paths in Heterogeneous Information NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303721834:9(4172-4185)Online publication date: 1-Sep-2022
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Zheng YShi CCao XLi XWu B(2022)A Meta Path Based Method for Entity Set Expansion in Knowledge GraphIEEE Transactions on Big Data10.1109/TBDATA.2018.28053668:3(616-629)Online publication date: 1-Jun-2022
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