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CHAMELEON: a deep learning meta-architecture for news recommender systems

Published: 27 September 2018 Publication History

Abstract

News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift.
Deep Learning (DL) have achieved a great success in complex domains, such as computer vision, Natural Language Processing (NLP), machine translation, speech recognition, and reinforcement learning. Therefore, it became a mainstream approach in Recommender Systems research only since 2016.
The main objective of this research is the investigation, design, implementation and evaluation of a Meta-Architecture for personalized news recommendations using deep neural networks.
As information about users' past interactions is scarce in such cold-start scenario, user context and session information are explicitly modeled, as well as past user sessions, when available. Users' past behaviors and item features are both considered in an hybrid session-aware recommendation approach. The recommendation task addressed in this work is next-item prediction for user sessions: "what is the next most likely article a user might read in a session?"
This paper presents the research methodology for this Doctoral research, the proposed Meta-Architecture and some preliminary results, as well as the next research challenges.

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cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 27 September 2018

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Author Tags

  1. context-based recommendation
  2. deep learning
  3. meta-architecture
  4. news recommendation
  5. recommender systems
  6. recurrent neural networks
  7. session-based recommendation

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  • Extended-abstract

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RecSys '18
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RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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  • (2023)Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social NetworksACM Transactions on the Web10.1145/361608817:4(1-26)Online publication date: 14-Aug-2023
  • (2023)Deep learning in news recommender systems: A comprehensive survey, challenges and future trendsNeurocomputing10.1016/j.neucom.2023.126881562(126881)Online publication date: Dec-2023
  • (2023)SJORS: A Semantic Recommender System for JournalistsBusiness & Information Systems Engineering10.1007/s12599-023-00843-666:6(691-708)Online publication date: 21-Dec-2023
  • (2023)Leveraging Sequential Episode Mining for Session-Based News RecommendationWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_46(594-608)Online publication date: 21-Oct-2023
  • (2023)Improving Accuracy of Recommendation Systems with Deep Learning ModelsAdvances in Data-Driven Computing and Intelligent Systems10.1007/978-981-99-3250-4_60(795-806)Online publication date: 4-Aug-2023
  • (2023)Hybrid/Advanced Session-Based Recommender SystemsSession-Based Recommender Systems Using Deep Learning10.1007/978-3-031-42559-2_5(171-244)Online publication date: 21-Dec-2023
  • (2023)Modeling Users’ Localized Preferences for More Effective News RecommendationArtificial Intelligence in HCI10.1007/978-3-031-35894-4_27(366-382)Online publication date: 9-Jul-2023
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  • (2021)News recommender system: a review of recent progress, challenges, and opportunitiesArtificial Intelligence Review10.1007/s10462-021-10043-xOnline publication date: 21-Jul-2021
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