Recommender Systems (RecSys)

Motivation and Objectives

Recommender Systems (RS) support complex decision processes for identifying products, services or other types of items to users by profiling users and items in diverse ways. Online information systems have led to an abundance of data that is heterogeneous, noisy, and changes rapidly. The data used by recommender systems, in forms of implicit or explicit user feedback, follow the same trend: the feedback can be in various formats, such as ratings, online behaviors, or textual reviews, and collected from multiple resources. Besides this, the collected feedback is uncertain, and user taste and item popularities can change over time. The challenge is to develop, deploy and evaluate systems that provide users with meaningful guidance in order to help identify suitable items resources from a potentially overwhelming variety of choices.

The aim of this session is to bring together researchers and practitioners that are working on topics related to the design, development and testing of recommender systems as well as present the current status of research in this area and promoting collaboration opportunities between researchers and practitioners.

Topics of Interest

This session welcomes novel, interesting, high standard contributions from the following topics, but not limited to:

• Recommender Systems machine learning algorithms and optimization
• Cross-domain recommendation
• Context-aware recommendation, time-aware recommendation
• Multi-criteria recommender systems
• Evaluation of Recommender Systems
• Recommender Systems applications: marketing, tourism, health, e-Learning, law, news, social networks

Track chairs

Program Committee