US-NCF
Overview
- The problem of
information overloading
is prevalent in recommendations websites andsocial 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 withsocial
counterparts.
- However, current systems are unaware of the user’s additional
- US-NCF is a
context-aware deep learning-based recommender system
, in support for social recommender systems.
publications: IEEE ICC 2021