44 citations · 54 across the 5 of their papers we have counts for
5 papers
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs
Marios Papachristou, Rishab Goel, Frank Portman +2
In graph learning, there have been two predominant inductive biases regarding graph-inspired architectures: On the one hand, higher-order interactions and message passing work well…
MiCRO: Multi-interest Candidate Retrieval Online
Frank Portman, Stephen Ragain, Ahmed El-Kishky
Providing personalized recommendations in an environment where items exhibit ephemerality and temporal relevancy (e.g. in social media) presents a few unique challenges: (1) induct…
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
Ahmed El-Kishky, Thomas Markovich, Kenny Leung +3
Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate item…
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
Ahmed El-Kishky, Thomas Markovich, Serim Park +8
Social networks, such as Twitter, form a heterogeneous information network (HIN) where nodes represent domain entities (e.g., user, content, advertiser, etc.) and edges represent o…
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Luca Belli, Sofia Ira Ktena, Alykhan Tejani +12
Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is…