121 lines
4.9 KiB
Markdown
121 lines
4.9 KiB
Markdown
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# 基于图神经网络的推荐算法
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## 数据集
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oag-cs - 使用OAG微软学术数据构造的计算机领域的学术网络（见 [readme](data/readme.md)）
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## 预训练顶点嵌入
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使用metapath2vec（随机游走+word2vec）预训练顶点嵌入，作为GNN模型的顶点输入特征
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1. 随机游走
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```shell
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python -m gnnrec.kgrec.random_walk model/word2vec/oag_cs_corpus.txt
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```
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2. 训练词向量
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```shell
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python -m gnnrec.hge.metapath2vec.train_word2vec --size=128 --workers=8 model/word2vec/oag_cs_corpus.txt model/word2vec/oag_cs.model
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```
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## 召回
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使用微调后的SciBERT模型（见 [readme](data/readme.md) 第2步）将查询词编码为向量，与预先计算好的论文标题向量计算余弦相似度，取top k
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```shell
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python -m gnnrec.kgrec.recall
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```
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召回结果示例：
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graph neural network
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```
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0.9629 Aggregation Graph Neural Networks
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0.9579 Neural Graph Learning: Training Neural Networks Using Graphs
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0.9556 Heterogeneous Graph Neural Network
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0.9552 Neural Graph Machines: Learning Neural Networks Using Graphs
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0.9490 On the choice of graph neural network architectures
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0.9474 Measuring and Improving the Use of Graph Information in Graph Neural Networks
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0.9362 Challenging the generalization capabilities of Graph Neural Networks for network modeling
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0.9295 Strategies for Pre-training Graph Neural Networks
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0.9142 Supervised Neural Network Models for Processing Graphs
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0.9112 Geometrically Principled Connections in Graph Neural Networks
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```
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recommendation algorithm based on knowledge graph
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```
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0.9172 Research on Video Recommendation Algorithm Based on Knowledge Reasoning of Knowledge Graph
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0.8972 An Improved Recommendation Algorithm in Knowledge Network
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0.8558 A personalized recommendation algorithm based on interest graph
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0.8431 An Improved Recommendation Algorithm Based on Graph Model
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0.8334 The Research of Recommendation Algorithm based on Complete Tripartite Graph Model
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0.8220 Recommendation Algorithm based on Link Prediction and Domain Knowledge in Retail Transactions
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0.8167 Recommendation Algorithm Based on Graph-Model Considering User Background Information
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0.8034 A Tripartite Graph Recommendation Algorithm Based on Item Information and User Preference
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0.7774 Improvement of TF-IDF Algorithm Based on Knowledge Graph
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0.7770 Graph Searching Algorithms for Semantic-Social Recommendation
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```
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scholar disambiguation
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```
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0.9690 Scholar search-oriented author disambiguation
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0.9040 Author name disambiguation in scientific collaboration and mobility cases
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0.8901 Exploring author name disambiguation on PubMed-scale
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0.8852 Author Name Disambiguation in Heterogeneous Academic Networks
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0.8797 KDD Cup 2013: author disambiguation
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0.8796 A survey of author name disambiguation techniques: 2010–2016
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0.8721 Who is Who: Name Disambiguation in Large-Scale Scientific Literature
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0.8660 Use of ResearchGate and Google CSE for author name disambiguation
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0.8643 Automatic Methods for Disambiguating Author Names in Bibliographic Data Repositories
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0.8641 A brief survey of automatic methods for author name disambiguation
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```
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## 精排
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### 构造ground truth
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（1）验证集
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从AMiner发布的 [AI 2000人工智能全球最具影响力学者榜单](https://www.aminer.cn/ai2000) 抓取人工智能20个子领域的top 100学者
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```shell
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pip install scrapy>=2.3.0
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cd gnnrec/kgrec/data/preprocess
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scrapy runspider ai2000_crawler.py -a save_path=/home/zzy/GNN-Recommendation/data/rank/ai2000.json
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```
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与oag-cs数据集的学者匹配，并人工确认一些排名较高但未匹配上的学者，作为学者排名ground truth验证集
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```shell
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export DJANGO_SETTINGS_MODULE=academic_graph.settings.common
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export SECRET_KEY=xxx
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python -m gnnrec.kgrec.data.preprocess.build_author_rank build-val
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```
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（2）训练集
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参考AI 2000的计算公式，根据某个领域的论文引用数加权求和构造学者排名，作为ground truth训练集
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计算公式：
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即：假设一篇论文有n个作者，第k作者的权重为1/k，最后一个视为通讯作者，权重为1/2，归一化之后计算论文引用数的加权求和
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```shell
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python -m gnnrec.kgrec.data.preprocess.build_author_rank build-train
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```
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（3）评估ground truth训练集的质量
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```shell
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python -m gnnrec.kgrec.data.preprocess.build_author_rank eval
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```
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```
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nDGC@100=0.2420 Precision@100=0.1859 Recall@100=0.2016
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nDGC@50=0.2308 Precision@50=0.2494 Recall@50=0.1351
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nDGC@20=0.2492 Precision@20=0.3118 Recall@20=0.0678
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nDGC@10=0.2743 Precision@10=0.3471 Recall@10=0.0376
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nDGC@5=0.3165 Precision@5=0.3765 Recall@5=0.0203
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```
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（4）采样三元组
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从学者排名训练集中采样三元组(t, ap, an)，表示对于领域t，学者ap的排名在an之前
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```shell
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python -m gnnrec.kgrec.data.preprocess.build_author_rank sample
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```
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### 训练GNN模型
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```shell
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python -m gnnrec.kgrec.train model/word2vec/oag-cs.model model/garec_gnn.pt data/rank/author_embed.pt
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```
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