ORSUM 2021-4th Workshop on Online Recommender Systems and User Modeling

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Vinagre, J (Author) Other; Jorge, AM (Author) FCUP; Al Ghossein, M (Author) Other; Bifet, A (Author) Other

Conference proceedings International

Title: 15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021)

Pages: 792-793

15th ACM Conference on Recommender Systems (RECSYS)

Amsterdam, NETHERLANDS, SEP 27-OCT 01, 2021

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Other information

Authenticus ID: P-00V-FQ3

DOI: 10.1145/3460231.3470940

Abstract (EN): Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content - e.g. posts, news, products, comments -, but also user feedback - e.g. ratings, views, reads, clicks -, together with context data - user device, spacial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of 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 and explainability.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 2