research-article

ICE: Item Concept Embedding via Textual Information

Authors:

Ting-Hsiang Wang,

Ming-Feng TsaiAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 85 - 94

https://doi.org/10.1145/3077136.3080807

Published: 07 August 2017 Publication History

Abstract

This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information. Specifically, in the proposed framework there are two stages: graph construction and embedding learning. In the first stage, we propose a generalized network construction method to build a network involving heterogeneous nodes and a mixture of both homogeneous and heterogeneous relations. The second stage leverages the concept of neighborhood proximity to learn the embeddings of both items and words. With the proposed carefully designed ICE networks, the resulting embedding facilitates both homogeneous and heterogeneous retrieval, including item-to-item and word-to-item retrieval. Moreover, as a distributed embedding approach, the proposed ICE approach not only generates related retrieval results but also delivers more diverse results than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, ICE encodes useful textual information and thus outperforms traditional methods in various item classification and retrieval tasks.

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Cited By

Wang XLiang JXiao YWang W(2022)Prototypical Concept RepresentationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3180886(1-14)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3180886
Wang TYang HChen CTsai MWang C(2022)Item Concept Network: Towards Concept-Based Item Representation LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299585934:3(1258-1274)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2995859
Deng Y(2022)Recommender Systems Based on Graph Embedding Techniques: A ReviewIEEE Access10.1109/ACCESS.2022.317419710(51587-51633)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3174197
Show More Cited By

Index Terms

ICE: Item Concept Embedding via Textual Information
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining
      1. Nearest-neighbor search

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Published In

cover image ACM Conferences

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

Copyright © 2017 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 07 August 2017

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Ministry of Science and Technology, Taiwan

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

Acceptance Rates

SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

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

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Cited By

Wang XLiang JXiao YWang W(2022)Prototypical Concept RepresentationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3180886(1-14)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3180886
Wang TYang HChen CTsai MWang C(2022)Item Concept Network: Towards Concept-Based Item Representation LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299585934:3(1258-1274)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2995859
Deng Y(2022)Recommender Systems Based on Graph Embedding Techniques: A ReviewIEEE Access10.1109/ACCESS.2022.317419710(51587-51633)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3174197
Reiter-Haas MWittenbrink DLacic E(2020)On the Heterogeneous Information Needs in the Job Domain: A Unified Platform for Student CareerProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3411554(573-574)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3411554
Gao MHe XChen LLiu TZhang JZhou A(2020)Learning Vertex Representations for Bipartite NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.2979980(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2020.2979980
Ma HHuang JAlhajj R(2020)User preference translation model for recommendation system with item influence diffusion embeddingProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381410(50-54)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/ASONAM49781.2020.9381410
Chen CWang TWang CTsai MBogers TSaid ABrusilovsky PTikk D(2019)SMOReProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346953(582-583)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3346953
Gao MChen LHe XZhou ACollins-Thompson KMei QDavison BLiu YYilmaz E(2018)BiNEThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3209987(715-724)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3209987
Lai KChen CTsai MWang C(2018)NavWalker: Information Augmented Network Embedding2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2018.0-113(9-16)Online publication date: Dec-2018
https://doi.org/10.1109/WI.2018.0-113
Ye QZhu CLi GLiu ZWang F(2018)Using Node Identifiers and Community Prior for Graph-Based ClassificationData Science and Engineering10.1007/s41019-018-0062-83:1(68-83)Online publication date: 16-Mar-2018
https://doi.org/10.1007/s41019-018-0062-8
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