毕业设计 基于Python+Django+Scrapy知识图谱的音乐推荐系统的设计与实现+全部资料齐全+部署文档.zip

# mkr-recommendation MKR is a Multi-task learning approach for Knowledge graph enhanced Recommendation. MKR consists of two parts: the recommender system (RS) module and the knowledge graph embedding (KGE) module. The two modules are bridged by cross&compress units, which can automatically learn high-order interactions of item and entity features and transfer knowledge between the two tasks.  # Usage For movie: python preprocess.py -d movie python main.py -dataset movie python predict_test.py -d movie # Testing the .pd model For book: python preprocess.py -d book python main.py -dataset book python predict_test.py -d book # Testing the .pd model For music: python preprocess.py -d music python main.py -dataset music python predict_test.py -d music # Testing the .pd model # File structure * model/ * movie/, book/, music/ * restore/: model save recovery save/restore method, use it to restore model weights * result/: save the .pd model, deploy model using tensorflow serving * vocab/: save the embedding, in order to transfer weight, use it for iterative training if new users or new movie/music/book join * data/ * book/ * BX-Book-Ratings.csv: raw rating file of Book-Crossing dataset * item_index2entity_id.txt: the mapping from item indices in the raw rating file to entity IDs in the KG * kg.txt: knowledge graph file * movie/ * item_index2entity_id.txt: the mapping from item indices in the raw rating file to entity IDs in the KG * kg.txt: knowledge graph file * ratrings.dat: raw rating file of MovieLens-1M * music/ * item_index2entity_id.txt: the mapping from item indices in the raw rating file to entity IDs in the KG * kg.txt: knowledge graph file * user_artists.dat: raw rating file of Last.FM # Reference [hwwang55/MKR](https://github.com/hwwang55/MKR) [Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. In Proceedings of The 2019 Web Conference (WWW 2019)](https://arxiv.org/abs/1901.08907)