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.
These challenges motivate the research on incremental, on-line methods that adapt to incoming data without retraining models from scratch.
Online learning algorithms and data stream mining have gained maturity in recent years, however they have not been thoroughly studied in
recommendation, and need further investigation.
Modern web-based systems continuously generate data at very fast rates. This continuous flow of data encompasses web content - e.g. posts,
news, products, comments -, but also user feedback -- e.g. ratings, views, reads, clicks, thumbs up -, as well as context information -
device, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design
to work in batch, given the fast rate of change of content, usage patterns and contextual variables. User modeling, recommendation and
personalization algorithms exploit content, user feedback and context, and can particularly benefit from algorithms capable of maintaining models
incrementally and online, as data is generated.
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
We welcome original, unpublished work in the form of either long and short paper submissions via EasyChair at:
Long papers must not exceed 16 pages (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
here (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.
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.
The proceedings will be published as a dedicated volume of Proceedings of Machine Learning Research
Papers will be published under the Creative Commons Attribution 4.0 International License, as specified
(human readable summary here).
Corresponding authors of accepted papers will need to submit PMLR license agreement form
together with the camera-ready version of the paper.