Python-KGAT用于推荐的知识图注意网络

# Knowledge Graph Attention Network This is our Tensorflow implementation for the paper: >Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu and Tat-Seng Chua (2019). KGAT: Knowledge Graph Attention Network for Recommendation. [Paper in ACM DL](https://dl.acm.org/authorize.cfm?key=N688414) or [Paper in arXiv](https://arxiv.org/abs/1905.07854). In KDD'19, Anchorage, Alaska, USA, August 4-8, 2019. Author: Dr. Xiang Wang (xiangwang at u.nus.edu) ## Introduction Knowledge Graph Attention Network (KGAT) is a new recommendation framework tailored to knowledge-aware personalized recommendation. Built upon the graph neural network framework, KGAT explicitly models the high-order relations in collaborative knowledge graph to provide better recommendation with item side information. ## Citation If you want to use our codes and datasets in your research, please cite: ``` @inproceedings{KGAT19, author = {Xiang Wang and Xiangnan He and Yixin Cao and Meng Liu and Tat{-}Seng Chua}, title = {{KGAT:} Knowledge Graph Attention Network for Recommendation}, booktitle = {{KDD}}, pages = {950--958}, year = {2019} } ``` ## Environment Requirement The code has been tested running under Python 3.6.5. The required packages are as follows: * tensorflow == 1.12.0 * numpy == 1.15.4 * scipy == 1.1.0 * sklearn == 0.20.0 ## Example to Run the Codes The instruction of commands has been clearly stated in the codes (see the parser function in Model/utility/parser.py). * Yelp2018 dataset ``` python Main.py --model_type kgat --alg_type bi --dataset yelp2018 --regs [1e-5,1e-5] --layer_size [64,32,16] --embed_size 64 --lr 0.0001 --epoch 400 --verbose 1 --save_flag 1 --pretrain -1 --batch_size 1024 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1] --use_att True --use_kge True ``` * Amazon-book dataset ``` python Main.py --model_type kgat --alg_type bi --dataset amazon-book --regs [1e-5,1e-5] --layer_size [64,32,16] --embed_size 64 --lr 0.0001 --epoch 400 --verbose 1 --save_flag 1 --pretrain -1 --batch_size 1024 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1] --use_att True --use_kge True ``` * Last-fm dataset ``` python Main.py --model_type kgat --alg_type bi --dataset last-fm --regs [1e-5,1e-5] --layer_size [64,32,16] --embed_size 64 --lr 0.0001 --epoch 400 --verbose 1 --save_flag 1 --pretrain -1 --batch_size 1024 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1] --use_att True --use_kge True ``` Some important arguments: * `model_type` * It specifies the type of model. * Here we provide six options, including KGAT and five baseline models: * `kgat` (by default), proposed in [KGAT: Knowledge Graph Attention Network for Recommendation](https://arxiv.org/abs/1905.07854), KDD2019. Usage: `--model_type kgat`. * `bprmf`, proposed in [BPR: Bayesian Personalized Ranking from Implicit Feedback](https://dl.acm.org/citation.cfm?id=1795167), UAI2009. Such model only uses user-item interactions. Usage: `--model_type bprmf`. * `fm`, proposed in [Fast context-aware recommendations with factorization machines](https://dl.acm.org/citation.cfm?id=2010002), SIGIR2011. Usage: `--model_type fm`. * `nfm`, proposed in [Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/citation.cfm?id=3080777), SIGIR2017. Usage: `--model_type nfm`. * `cke`, proposed in [Collaborative Knowledge Base Embedding for Recommender Systems](https://dl.acm.org/citation.cfm?id=2939673), KDD2016. Usage: `--model_type cke`. * `cfkg`, proposed in [Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation](https://arxiv.org/abs/1805.03352), Algorithm2018. Usage: `--model_type cfkg`. * You can find other baselines, such as RippleNet, MCRec, and GC-MC, in Github. * `alg_type` * It specifies the type of graph convolutional layer. * Here we provide three options: * `kgat` (by default), proposed in [KGAT: Knowledge Graph Attention Network for Recommendation](xx), KDD2019. Usage: `--alg_type kgat`. * `gcn`, proposed in [Semi-Supervised Classification with Graph Convolutional Networks](https://openreview.net/pdf?id=SJU4ayYgl), ICLR2018. Usage: `--alg_type gcn`. * `graphsage`, propsed in [Inductive Representation Learning on Large Graphs.](https://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf), NeurIPS2017. Usage: `--alg_type graphsage`. * `adj_type` * It specifies the type of laplacian matrix where each entry defines the decay factor between two connected nodes. * Here we provide two options: * `si` (by default), where each decay factor between two connected nodes (say, x->y) is set as 1/(out degree of x), while each node is also assigned with 1 for self-connections. Usage: `--adj_type si`. * `bi`, where each decay factor between two connected nodes (say, x->y) is set as 1/sqrt((out degree of x)*(out degree of y)). Usage: `--adj_type bi`. * `mess_dropout` * It indicates the message dropout ratio, which randomly drops out the outgoing messages. Usage `--mess_dropout [0.1,0.1,0.1]`. ## Dataset We provide three processed datasets: Amazon-book, Last-FM, and Yelp2018. * You can find the full version of recommendation datasets via [Amazon-book](http://jmcauley.ucsd.edu/data/amazon), [Last-FM](http://www.cp.jku.at/datasets/LFM-1b/), and [Yelp2018](https://www.yelp.com/dataset/challenge). * We follow [KB4Rec](https://github.com/RUCDM/KB4Rec) to preprocess Amazon-book and Last-FM datasets, mapping items into Freebase entities via title matching if there is a mapping available. | | | Amazon-book | Last-FM | Yelp2018 | |:---:|:---|---:|---:|---:| |User-Item Interaction| #Users | 70,679 | 23,566 | 45,919| | | #Items | 24,915 | 48,123 | 45,538| | | #Interactions | 847,733 | 3,034,796 | 1,185,068| |Knowledge Graph | #Entities | 88,572 | 58,266 | 90,961| | | #Relations | 39 | 9 | 42 | | | #Triplets | 2,557,746 | 464,567 | 1,853,704| * `train.txt` * Train file. * Each line is a user with her/his positive interactions with items: (`userID` and `a list of itemID`). * `test.txt` * Test file (positive instances). * Each line is a user with her/his positive interactions with items: (`userID` and `a list of itemID`). * Note that here we treat all unobserved interactions as the negative instances when reporting performance. * `user_list.txt` * User file. * Each line is a triplet (`org_id`, `remap_id`) for one user, where `org_id` and `remap_id` represent the ID of such user in the original and our datasets, respectively. * `item_list.txt` * Item file. * Each line is a triplet (`org_id`, `remap_id`, `freebase_id`) for one item, where `org_id`, `remap_id`, and `freebase_id` represent the ID of such item in the original, our datasets, and freebase, respectively. * `entity_list.txt` * Entity file. * Each line is a triplet (`freebase_id`, `remap_id`) for one entity in knowledge graph, where `freebase_id` and `remap_id` represent the ID of such entity in freebase and our datasets, respectively. * `relation_list.txt` * Relation file. * Each line is a triplet (`freebase_id`, `remap_id`) for one relation in knowledge graph, where `freebase_id` and `remap_id` represent the ID of such relation in freebase and our datasets, respectively. ## Acknowledgement Any scientific publications that use our datasets should cite the following paper as the reference: ``` @inproceedings{KGAT19, author = {Xiang Wang and Xiangnan He and Yixin Cao and Meng Liu and Tat-Seng Chua}, title = {KGAT: Knowledge Graph Attention Network for Recommendation}, booktitle = {{KDD}}, year = {2019} } ``` Nobody guarantees the correctness of the data, its suitability for any particular purpose, or the validity of results based on the use of the data set. The data set may be used for any research purposes under the following conditions: * Th