CLEF NewsREEL 2017 Overview: Contextual Bandit News Recommendation

Yu Liang, Babak Loni, Martha Larson

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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In the CLEF NewsREEL 2017 challenge, we build a delegation model based on the contextual bandit algorithm. Our goal is to investigate whether a bandit approach combined with context extracted from the user side, from the item side and from user-item interaction can help choose the appropriate recommender from a recommender algorithm pool for the incoming recommendation requests. We took part in both tasks: NewsREEL Live and NewsREEL Replay. In the experiment, we test several bandit approaches with two types of context features. The result from NewsREEL Replay suggests that delegation model based on the contextual bandit algorithm can improve the click through rate (CTR). In NewsREEL Live, a similar delegation model is implemented. However, the delegation model from NewsREEL Live is trained by the data stream from NewsREEL Replay. This is due to the fact that the low volume of data received from the online scenario is not enough to support the training of the delegation model. For our future work, we will add more recommender algorithms to the recommender algorithm pool and explores other context features.
Original languageEnglish
Title of host publicationCLEF 2017 Working Notes
Subtitle of host publicationConference and Labs of the Evaluation Forum
EditorsLinda Cappellato, Nicola Ferro, Lorraine Goeuriot, Thomas Mandl
Number of pages13
Publication statusPublished - 2017
EventCLEF 2017 - Conference and Labs of the Evaluation Forum: Information Access Evaluation meets Multilinguality, Multimodality, amd Visualization - Dublin, Ireland
Duration: 11 Sep 201714 Sep 2017
Conference number: 8

Publication series

NameCEUR Workshop Proceedings
ISSN (Electronic)1613-0073


ConferenceCLEF 2017 - Conference and Labs of the Evaluation Forum
Internet address


  • Recommender System
  • Context
  • Contextual Bandit
  • News
  • Evaluation


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