[dbitaly] CfP - 1st International Workshop on LEARning Next gEneration Rankers (LEARNER2017) co-located with ICTIR 2017

Nicola Ferro ferro a dei.unipd.it
Lun 5 Giu 2017 10:20:21 CEST

Call for papers
1st International Workshop on LEARning Next gEneration Rankers (LEARNER2017)
co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017), October 1, 2017, Amsterdam, The Netherlands
Learning to Rank (LtR), and machine learning in general, have proven to be very effective methodologies to address the increasing complexity of information systems, significantly improving over state-of-the-art traditional algorithms. Popular areas of investigation in LtR are related to efficiency, feature selection, supervised learning, but many new angles are still overlooked. The goal of this workshop is to investigate how to improve ranking, in particular LtR, by bringing in new perspectives which have not explored or fully addressed yet.
In particular, we wish to encourage researchers to discuss the opportunities, challenges, results obtained in the development and evaluation of novel approaches to LtR. New perspectives on LtR may concern innovative models, study of their formal properties as well as experimental validation of their efficiency and effectiveness. We are in particular interested in proposal dealing with novel LtR algorithms, evaluation of LtR algorithms, LtR dataset creation and curation, and domain specific applications of LtR.
We invite papers from researchers and practitioners working in Information Retrieval, Machine Learning and related application areas to submit their original papers to this workshop.
Topics of interest include (but are not limited to):
* Next Generation LtR Algorithms:
    - Unsupervised approaches to LtR, active learning for LtR, transfer learning for LtR;
    - Incremental LtR, online, or personalized LtR;
    - Embedding user behaviour and dynamic in LtR;
    - Cost-Aware LtR;
    - List-based approaches for result list diversification and/or clustering;
    - Bias/Variance and other theoretical characterizations or ranking models;
    - Feature engineering for ranking;
    - Deep neural networks for ranking;
    - Understanding and explaining complex LtR models, also via visual analytics solutions.
* Evaluation of LtR Algorithms:
    - Quality measures accounting for user behaviour and perceived quality;
    - Quality measures accounting for models failures, redundancy, robustness, sensitivity, etc.;
    - Evaluation of ranking efficiency vs. quality trade-off;
    - Visual analytics solutions for exploring and interpreting experimental data;
    - Reproducibility of LtR experiments.
* Datasets:
    - Measuring quality of training datasets: noise, contradictory examples, redundancy, difficulty of building a good model, features quality, coverage of application domain use cases;
    - Creation and curation of datasets: compression, negative sampling, aging, dimensionality reduction;
    - Contributing novel datasets to the community.
* Applications:
    - Application of LtR to verticals or to other domains (e.g., recommendation, news, product search, social media, job search, ...);
    - LtR beyond documents: keyword-based access to structured data, multimedia, graphs, etc.
We invite four-six pages, single-blind submissions formatted according to the ACM SIG Proceedings Template (http://www.acm.org/publications/proceedings-template).  
Papers should be submitted in PDF format to the following address: 


* Nicola Ferro, University of Padua, Italy
* Claudio Lucchese, ISTI-CNR, Italy
* Maria Maistro, University of Padua, Italy
* Raffaele Perego, ISTI-CNR, Italy
* Papers Due: August 14, 2017
* Notification of Acceptance: September 1, 2017
* Camera Ready: September 8, 2017
* Workshop: October 1, 2017

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