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HeteRecom: a semantic-based recommendation system in heterogeneous networks

Published: 12 August 2012 Publication History

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.

References

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J. Han. Mining heterogeneous information networks by exploring the power of links. In DS, pages 13--30, 2009.
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N. Lao and W. Cohen. Fast query execution for retrieval models based on path constrained random walks. In KDD, pages 881--888, 2010.
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M. Shang, L. Lu, Y. Zhang, and T. Zhou. Empirical analysis of web-based user-object bipartite networks. In EPL 90 (0120) 48006, 2010.
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C. Shi, X. Kong, P. S. Yu, and S. Xie. Relevance search in heterogeneous networks. In EDBT, 2012.
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Y. Sun, J. Han, X. Yan, P. Yu, and T. Wu. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. In VLDB, pages 992--1003, 2011.

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  • (2025)MetapathVis: Inspecting the Effect of Metapath in Heterogeneous Network Embedding via Visual AnalyticsComputer Graphics Forum10.1111/cgf.15285Online publication date: 31-Jan-2025
  • (2024)Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368197517:11(2946-2959)Online publication date: 30-Aug-2024
  • (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
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    cover image ACM Conferences
    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
    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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    Publication History

    Published: 12 August 2012

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    Author Tags

    1. heterogeneous information network
    2. recommendation
    3. semantic search
    4. similarity

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    View all
    • (2025)MetapathVis: Inspecting the Effect of Metapath in Heterogeneous Network Embedding via Visual AnalyticsComputer Graphics Forum10.1111/cgf.15285Online publication date: 31-Jan-2025
    • (2024)Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368197517:11(2946-2959)Online publication date: 30-Aug-2024
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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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