Zhaoyue Cheng
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View article: Session-based Recommendation with Transformers
Session-based Recommendation with Transformers Open
Large item catalogs and constantly changing preference trends make recommendations a critically important component of every fashion e-commerce platform. However, since most users browse anonymously, historical preference data is rarely av…
View article: MCL: Mixed-Centric Loss for Collaborative Filtering
MCL: Mixed-Centric Loss for Collaborative Filtering Open
The majority of recent work in latent Collaborative Filtering (CF) has focused on developing new model architectures to learn accurate user and item representations. Typically, a standard pairwise loss function (BPR, Triplet, etc.) is used…
View article: User Engagement Modeling with Deep Learning and Language Models
User Engagement Modeling with Deep Learning and Language Models Open
Twitter is one of the main information sharing platforms in the world with millions of tweets created daily. To ensure that users get relevant content in their feeds Twitter extensively leverages machine learning-based recommender systems.…
View article: HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering
HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering Open
Hyperbolic spaces offer a rich setup to learn embeddings with superior properties that have been leveraged in areas such as computer vision, natural language processing and computational biology. Recently, several hyperbolic approaches hav…
View article: Predicting Twitter Engagement With Deep Language Models
Predicting Twitter Engagement With Deep Language Models Open
Twitter has become one of the main information sharing platforms for millions of users world-wide. Numerous tweets are created daily, many with highly time sensitive content such as breaking news, new multimedia content or personal updates…
View article: TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations
TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations Open
Collaborative filtering with implicit feedback is a ubiquitous class of recommendation problems where only positive interactions such as purchases or clicks are observed. Autoencoder-based recommendation models have shown strong performanc…