US-NCF

Overview

• The problem of information overloading is prevalent in recommendations websites and social networks.
• Users seek relevant recommendations from like-minded connections. User-item interactions (i.e., ratings) are prevalent in recommendation websites such as Netflix, whereas user-user connections are the interaction sought in social websites such as Twitter.
• Social recommender systems seek to generate recommendations for users based on similar preferences of their close friends.
• Because social networks do not normally contain user-item interactions, social recommender systems are typically hybridized with other recommenders (e.g., website recommenders such as Netflix) that provide such interaction.
  • However, current systems are unaware of the user’s additional contextual information when coupled with social counterparts.
• US-NCF is a context-aware deep learning-based recommender system, in support for social recommender systems.

publications: IEEE ICC 2021