Latent Relational Metric Learning via Memory-based Attention for Collaborative RankingLatent Relational Metric Learning via Memory-based Attention for Collaborative RankingYi Tay and Luu Anh Tuan and Siu Cheung Hui2018
Paper summarydarelThis work is a direct improvement of Collaborative Metric Learning. While CML tries to retrieve user and item embeddings in a direct way by placing them in metric space and adjusting with triplet loss, this paper focuses on introduction of latent relational vectors.
A relational vector $r$ must describe relation between user $p$ and item $q$ in a way that $s(p,q)=\parallel \ p + r - q \parallel \approx 0$.
Vectors $r$ are introduced as a softmax-weighted linear combination of vectors from Latent Relational Attentive Memory (LRAM).
The net is trained with BPR-like loss via negative sampling $max(0, s(p,q) - s(p', q') + margin)$
This work is a direct improvement of Collaborative Metric Learning. While CML tries to retrieve user and item embeddings in a direct way by placing them in metric space and adjusting with triplet loss, this paper focuses on introduction of latent relational vectors.
A relational vector $r$ must describe relation between user $p$ and item $q$ in a way that $s(p,q)=\parallel \ p + r - q \parallel \approx 0$.
Vectors $r$ are introduced as a softmax-weighted linear combination of vectors from Latent Relational Attentive Memory (LRAM).
The net is trained with BPR-like loss via negative sampling $max(0, s(p,q) - s(p', q') + margin)$