Introduction ====================== hopwise ---------------------- hopwise is an advanced extension of the **RecBole** library, designed to enhance recommendation systems with the power of knowledge graphs. By integrating knowledge embedding models, path-based reasoning methods, and path language modeling approaches, hopwise supports both recommendation and link prediction tasks with a focus on explainability. .. image:: asset/hopwise.png :width: 600 :align: center **It aims to help the researchers to reproduce and develop recommendation models.** In the lastest release, our library includes all the algorithms already present in Recbole `[Model List]`_, along with two 🆕 new categories of models and numerous other improvements: - General Recommendation - Sequential Recommendation - Context-aware Recommendation - Knowledge-based Recommendation - Path Reasoning based Recommendation - Knowledge Graph Embeddings for Recommendation and Link prediction We have also added 4 new datasets in addition to the 44 datasets already available in Recbole `[Collected Datasets]`_. New Features: - We added 7 Path-Based (some of them from scratch) - We added 14 knowledge graph embedding methods - We added 4 new datasets - We added 12 new metrics covering Beyond-Accuracy and Path-Quality Metrics - We added the feature to evaluate from a checkpoint - We added the support for Link Prediction along the recommendation task on KGE - We added the support for optuna in hyperparameters hyper_tuning - We added a new data sample feature to sample paths from a knowledge graph We also covered the saving of different dataloaders so you don't need to sample each time new paths - We added support for uv - We added support for embeddings visualization through tSNE We also prepared a case study to show how to use it inside run_example folder .. _[Collected Datasets]: /dataset_list.html .. _[Model List]: /model_list.html .. toctree:: :maxdepth: 1 :caption: Get Started get_started/install get_started/distributed_training get_started/data_intro .. toctree:: :maxdepth: 1 :caption: User Guide user_guide/usage user_guide/configuration user_guide/data_intro user_guide/tasks_models_intro user_guide/training_and_evaluation user_guide/hyperparameters_tuning .. toctree:: :maxdepth: 1 :caption: Architecture developer_guide/change_configuration developer_guide/create_datasets developer_guide/create_samplers developer_guide/create_dataloaders developer_guide/create_trainers developer_guide/create_models developer_guide/create_metrics The Team ------------------ hopwise is developed and maintained by the **`Trustworthy Artificial Intelligence Laboratory @ University of Cagliari`**. License ------------ hopwise uses `MIT License `.