ORSUM 2022 - 5th Workshop on Online Recommender Systems and User Modeling

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Vinagre, J (Author) Other; Ghossein, MA (Author) Other; Jorge, AM (Author) FCUP; Bifet, A (Author) Other; Peska, L (Author) Other

Conference proceedings International

Title: RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022

Pages: 661-662

16th ACM Conference on Recommender Systems, RecSys 2022

18 September 2022 through 23 September 2022

Indexing

Scopus

Other information

Authenticus ID: P-00X-83J

DOI: 10.1145/3523227.3547411

Abstract (EN): Modern online systems for user modeling and recommendation need to continuously deal with complex data streams generated by users at very fast rates. This can be overwhelming for systems and algorithms designed to train recommendation models in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably for long periods of time. Online models that continuously learn from such flows of data are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy, fairness and transparency. © 2022 Owner/Author.

Language: English

Type (Professor's evaluation): Scientific