New publications that are scheduled to appear this month:

  • Mozhdeh Ariannezhad, Sebastian Schelter, and Maarten de Rijke. Demand Forecasting in the Presence of Privileged Information. In AALTD: International Workshop on Advanced Analytics and Learning on Temporal Data, LNCS 12588, pages 46-62. Springer, December 2020. Bibtex, PDF
    @inproceedings{ariannezhad-2020-demand,
    author = {Ariannezhad, Mozhdeh and Schelter, Sebastian and de Rijke, Maarten},
    booktitle = {AALTD: International Workshop on Advanced Analytics and Learning on Temporal Data},
    date-added = {2020-07-16 18:11:34 +0200},
    date-modified = {2020-12-29 23:04:14 +0100},
    month = {December},
    pages = {46-62},
    publisher = {Springer},
    series = {LNCS 12588},
    title = {Demand Forecasting in the Presence of Privileged Information},
    year = {2020}}
  • Jin Huang, Harrie Oosterhuis, Maarten de Rijke, and Herke van Hoof. Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. In RecSys 2020: The ACM Conference on Recommender Systems, page 190–199. ACM, September 2020. Bibtex, PDF
    @inproceedings{huang-2020-keeping,
    author = {Huang, Jin and Oosterhuis, Harrie and de Rijke, Maarten and van Hoof, Herke},
    booktitle = {RecSys 2020: The ACM Conference on Recommender Systems},
    date-added = {2020-07-23 08:03:19 +0200},
    date-modified = {2020-09-19 09:47:04 +0200},
    month = {September},
    pages = {190--199},
    publisher = {ACM},
    title = {Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems},
    year = {2020}}
  • Chang Li, Haoyun Feng, and Maarten de Rijke. Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity. In RecSys 2020: The ACM Conference on Recommender Systems, page 33–42. ACM, September 2020. Bibtex, PDF
    @inproceedings{li-2020-cascading,
    author = {Li, Chang and Feng, Haoyun and de Rijke, Maarten},
    booktitle = {RecSys 2020: The ACM Conference on Recommender Systems},
    date-added = {2020-07-23 07:41:55 +0200},
    date-modified = {2020-09-19 09:46:34 +0200},
    month = {September},
    pages = {33--42},
    publisher = {ACM},
    title = {Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity},
    year = {2020}}
  • Harrie Oosterhuis and Maarten de Rijke. Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. In ICTIR 2020: The 6th ACM International Conference on the Theory of Information Retrieval, page 137–144. ACM, September 2020. Bibtex, PDF
    @inproceedings{oosterhuis-2020-taking,
    author = {Oosterhuis, Harrie and de Rijke, Maarten},
    booktitle = {ICTIR 2020: The 6th ACM International Conference on the Theory of Information Retrieval},
    date-added = {2020-06-30 10:31:31 +0200},
    date-modified = {2020-09-15 06:54:46 +0200},
    month = {September},
    pages = {137--144},
    publisher = {ACM},
    title = {Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking},
    year = {2020}}
  • Chuan Wu, Evangelos Kanoulas, and Maarten de Rijke. It All Starts with Entities: A Salient Entity Topic Model. Natural Language Engineering, 26(5):531–549, September 2020. Bibtex, PDF
    @article{wu-2020-all,
    author = {Wu, Chuan and Kanoulas, Evangelos and de Rijke, Maarten},
    date-added = {2018-09-21 10:00:04 +0200},
    date-modified = {2020-08-12 06:43:39 +0200},
    journal = {Natural Language Engineering},
    month = {September},
    number = {5},
    pages = {531--549},
    title = {It All Starts with Entities: A Salient Entity Topic Model},
    volume = {26},
    year = {2020}}