Call for Papers

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