elliot.recommender.knowledge_aware.kaHFM_batch package¶
Submodules¶
elliot.recommender.knowledge_aware.kaHFM_batch.kahfm_batch module¶
Module description:
-
class
elliot.recommender.knowledge_aware.kaHFM_batch.kahfm_batch.
KaHFMBatch
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
Knowledge-aware Hybrid Factorization Machines (Tensorflow Batch Variant)
Vito Walter Anelli and Tommaso Di Noia and Eugenio Di Sciascio and Azzurra Ragone and Joseph Trotta “How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs”, ISWC 2019 Best student Research Paper For further details, please refer to the paper
Vito Walter Anelli and Tommaso Di Noia and Eugenio Di Sciascio and Azzurra Ragone and Joseph Trotta “Semantic Interpretation of Top-N Recommendations”, IEEE TKDE 2020 For further details, please refer to the paper
- Parameters
lr – learning rate (default: 0.0001)
l_w – Weight regularization (default: 0.005)
l_b – Bias regularization (default: 0)
To include the recommendation model, add it to the config file adopting the following pattern:
models: KaHFMBatch: meta: hyper_max_evals: 20 hyper_opt_alg: tpe validation_rate: 1 verbose: True save_weights: True save_recs: True validation_metric: nDCG@10 epochs: 100 batch_size: -1 lr: 0.0001 l_w: 0.005 l_b: 0
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property
name
¶
elliot.recommender.knowledge_aware.kaHFM_batch.kahfm_batch_model module¶
Module description:
-
class
elliot.recommender.knowledge_aware.kaHFM_batch.kahfm_batch_model.
KaHFM_model
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model
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get_config
()[source]¶ Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).
- Returns
Python dictionary.
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