research-article

WG4Rec: Modeling Textual Content with Word Graph for News Recommendation

Authors:

Shaoping MaAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 1651 - 1660

https://doi.org/10.1145/3459637.3482401

Published: 30 October 2021 Publication History

Abstract

News recommendation plays an indispensable role in acquiring daily news for users. Previous studies make great efforts to model high-order feature interactions between users and items, where various neural models are applied (e.g., RNN, GNN). However, we find that seldom efforts are made to get better representations for news. Most previous methods simply adopt pre-trained word embeddings to represent news and also suffer from cold-start users.

In this work, we propose a new textual content representation method by building a word graph for recommendation, which is named WG4Rec. Three types of word associations are adopted in WG4Rec for content representation and user preference modeling, namely: 1)semantically-similar according to pre-trained word vectors, 2)co-occurrence in documents, and 3)co-click by users across documents. As extra information can be unified by adding nodes/edges to the word graph easily, WG4Rec is flexible to make use of cross-platform and cross-domain context for recommendation to alleviate the cold-start issue. To the best of our knowledge, it is the first attempt that using these relationships for news recommendation to better model textual content and adopt cross-platform information. Experimental results on two large-scale real-world datasets show that WG4Rec significantly outperforms state-of-the-art algorithms, especially for cold users in the online environment. Besides, WG4Rec achieves better performances when cross-platform information is utilized.

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Index Terms

WG4Rec: Modeling Textual Content with Word Graph for News Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
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Program Chairs:
J. Shane Culpepper
RMIT University, Australia
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Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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https://dl.acm.org/doi/10.1016/j.neunet.2024.106824
Liu ZChen ZZhang MDuan SWen HLi LLi NGu YYu GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Modeling User Viewing Flow using Large Language Models for Article RecommendationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648305(83-92)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648305
Pokusajev SStefanovič P(2024)A Roadmap on Developing a Taxonomy for Text Data Mining2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)10.1109/eStream61684.2024.10542606(1-5)Online publication date: 25-Apr-2024
https://doi.org/10.1109/eStream61684.2024.10542606
Chen BXu YZhen JHe XFang QCao J(2024)NRMG: News Recommendation With Multiview Graph Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326652011:2(2245-2255)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3266520
Zhao QChen XZhang HLi X(2024)Dynamic Hierarchical Attention Network for news recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124667255:PCOnline publication date: 1-Dec-2024
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Yu TZhang LLiu HLiu HWang J(2024)Service recommendation based on contrastive learning and multi-task learningComputer Communications10.1016/j.comcom.2023.11.018213:C(285-295)Online publication date: 27-Feb-2024
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Yang SWang CLiu YXu KMa WLiu YZhang MZeng HFeng JDeng C(2023)Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00082(728-737)Online publication date: 1-Dec-2023
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Wu HLong JLi NYu DNg M(2022)Adversarial Auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative HypergraphsACM Transactions on Information Systems10.1145/354410541:2(1-25)Online publication date: 21-Dec-2022
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