SR-GNN, 圖網絡召回

簡述

將用戶的session序列構建成有向圖,通過考慮item之間的轉移關係, 更好的學習item emb及用戶的向量表達.

related work

一些流行的GNN演變.
word2vec, DeepWalk, LINE, node2vec.

結構及解讀

網絡結構

nodes connection

$A_s$ is defined as the concatenation of two adjacency matrices. directed edges have normalized weight.

gated recurrent units

session emb

$hybrid\_session\_emb=linear([s_{local};s_{global}])$, where $s_{global}=soft\_attention(\cdot)$

experiment

• baselines. GRU4REC, STAMP, etc.
• dataset. YooChoose from RecSys Chanllenge 2015.
• P@20=71.36, evaluation metric , identical to recall@20.

參考

1. paper,AAAI,2019, Session-based Recommendation with Graph Neural Networks
2. corresponding official code,SR-GNN