推荐系统论文 Context-aware Graph Embedding for Session-based News Recommendation

Context-aware Graph Embedding for Session-based News Recommendation

RecSys 2020

Focus on session-based news recommendation

Abstract

Existing methods ignore the semantic-level structural information among news articles and do not explore external knowledge sources.

• propose a novel Context-aware Graph Embedding(CAGE) framework for session-based news recommendation
• builds an auxiliary KG to enrich the semantic meaning of entities, and further refines the article embeddings by graph convolutional networks

Model

1. extract entities from news articles
2. build sub-KG from open KG
3. obtain article content embeddings through word embedding and CNN
4. concatenate
5. feature refinement by GCN
6. RNN to obtain session-level embeddings
7. predictor

Textual-Level Article Embedding

Semantic-Level Embedding

Refining Article Embeddings with GNN

concate article_emb, user_feature_emb and semantic_emb

article emb:

p: user attributes

• articles with similar concepts shall be close in the embedding space

so contrust article-level graphs and employ GNN to refine embs.

1. articles are nodes
2. pair-wise similarity of article embeddings are weights on edges
3. remove edges with small similarity
4. two-layer GNN

D: degree matrix
A: adjacency matrix

Framework

Experments

• Dataset

Adressa + wididata KG