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The Adressa dataset for news recommendation

Authors:

Jon Atle Gulla,

Özlem Özgöbek,

Xiaomeng SuAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 1042 - 1048

https://doi.org/10.1145/3106426.3109436

Published: 23 August 2017 Publication History

Abstract

Datasets for recommender systems are few and often inadequate for the contextualized nature of news recommendation. News recommender systems are both time- and location-dependent, make use of implicit signals, and often include both collaborative and content-based components. In this paper we introduce the Adressa compact news dataset, which supports all these aspects of news recommendation. The dataset comes in two versions, the large 20M dataset of 10 weeks' traffic on Adresseavisen's news portal, and the small 2M dataset of only one week's traffic. We explain the structure of the dataset and discuss how it can be used in advanced news recommender systems.

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Cited By

Miao YPavlovski MChen ZZhou F(2025)Multi-Aspect Matching between Disentangled Representations of User Interests and Content for News RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_29(426-435)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_29
Jiang SSong HLu YZhang Z(2024)News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference ModelingApplied Sciences10.3390/app1501030015:1(300)Online publication date: 31-Dec-2024
https://doi.org/10.3390/app15010300
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Show More Cited By

Index Terms

The Adressa dataset for news recommendation
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. World Wide Web
    1. Web mining
    2. Web searching and information discovery

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Published In

cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 ACM.

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 the author(s) 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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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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Research Council of Norway

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WI '17

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SIGAI
TCII

WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Miao YPavlovski MChen ZZhou F(2025)Multi-Aspect Matching between Disentangled Representations of User Interests and Content for News RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_29(426-435)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_29
Jiang SSong HLu YZhang Z(2024)News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference ModelingApplied Sciences10.3390/app1501030015:1(300)Online publication date: 31-Dec-2024
https://doi.org/10.3390/app15010300
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
张雯(2024)Time-Sensitive Heterogeneous Graph Similarity Neural Network for News RecommendationComputer Science and Application10.12677/csa.2024.14408614:04(151-162)Online publication date: 2024
https://doi.org/10.12677/csa.2024.144086
Kruse JLindskow KKalloori SPolignano MPomo CSrivastava AUppal AAndersen MFrellsen J(2024)EB-NeRD a large-scale dataset for news recommendationProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687152(1-11)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687152
Möller LPadó S(2024)Explaining Neural News Recommendation with Attributions onto Reading HistoriesACM Transactions on Intelligent Systems and Technology10.1145/367323316:1(1-25)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3673233
Zhang GLi DGu HLu TGu N(2024)Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News RecommendationACM Transactions on the Web10.1145/364988618:3(1-33)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.1145/3649886
Iana AGlavaš GPaulheim HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)MIND Your Language: A Multilingual Dataset for Cross-lingual News RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657867(553-563)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657867
Fu JYuan FSong YYuan ZCheng MCheng SZhang JWang JPan YAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical InsightsProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635805(208-217)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635805
Jin JHe ZYang MZhang WYu YWang JMcAuley JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information MinimizationProceedings of the ACM Web Conference 202410.1145/3589334.3645356(1350-1361)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645356
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