Overview

The ever-growing nature of user generated data in online systems poses obvious challenges on how we process such data. Typically, this issue is regarded as a scalability problem and has been mainly addressed with distributed algorithms able to train on massive amounts of data in short time windows. However, data is inevitably adding up at high speeds. Eventually one needs to discard or archive some of it. Moreover, the dynamic nature of data in user modeling and recommender systems, such as change of user preferences, and the continuous introduction of new users and items make it increasingly difficult to maintain up-to-date, accurate recommendation models.

The objective of this workshop is to bring together researchers and practitioners interested in incremental and adaptive approaches to stream-based user modeling, recommendation and personalization, including algorithms, evaluation issues, incremental content and context mining, privacy and transparency, temporal recommendation or software frameworks for continuous learning.

Relevant topics include, but are not limited to:

- Incremental user modeling over data streams
- Incremental recommender systems
- Incremental web and text mining for personalization
- Online learning from user generated data
- Online learning from dynamic web content
- Online learning from multimedia content
- Online learning from social data
- Context-aware online learning
- Time-sensitive online learning
- Adaptive algorithms and interfaces
- Evaluation of online learning algorithms
- Architectures for continuous web data processing
- Explainable incremental algorithms
- Privacy-preserving incremental recommenders
- Online parameter optimization

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Important dates

2019-07-08: Paper submission deadline
2019-07-29: Paper acceptance notification
2019-08-27: Camera-ready paper deadline
2019-09-19: Workshop date

All deadlines are at 11:59pm AoE.

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Submissions

We welcome original, unpublished work in the form of either long and short paper submissions via EasyChair at:
https://easychair.org/conferences/?conf=orsum2019.

Long papers must not exceed 16 pages in single column, with up to 4 additional pages for references only, and should report research at a mature stage.

We also welcome the submission of preliminary results of ongoing research in the form of short papers with a maximum length of 8 pages, with 2 additional pages for references only.

Papers must be formatted in LaTeX and follow the template available at the workshop website (a Microsoft Word template will be made available upon request).

Review process is double-blind, so authors are required to remove any content that allows author identification.

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Publication

The proceedings will be published as a dedicated volume of Proceedings of Machine Learning Research (PMLR) series. The paper must be presented at the workshop by one of the authors. Presentations by proxy will be allowed in exceptional cases.

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Organization

João Vinagre
University of Porto
LIAAD - INESC TEC, Porto, Portugal

Alípio Mário Jorge
University of Porto
LIAAD - INESC TEC, Porto, Portugal

Albert Bifet
LTCI, Télécom ParisTech, France

Marie Al-Ghossein
LTCI, Télécom ParisTech, France


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Contact

E-mail: orsum2019@googlegroups.com
Twitter: https://twitter.com/orsum_ws