New publications

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}}