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Found 4 papers, 0 papers with code
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs
On the other hand, shallow (or node-level) models using ego features and adjacency embeddings work well in heterophilous graphs.
MiCRO: Multi-interest Candidate Retrieval Online
Providing personalized recommendations in an environment where items exhibit ephemerality and temporal relevancy (e. g. in social media) presents a few unique challenges: (1) inductively understanding ephemeral appeal for items in a setting where new items are created frequently, (2) adapting to trends within engagement patterns where items may undergo temporal shifts in relevance, (3) accurately modeling user preferences over this item space where users may express multiple interests.
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval.
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives.
