elliot.recommender.knowledge_aware.kaHFM package¶
Submodules¶
elliot.recommender.knowledge_aware.kaHFM.kahfm module¶
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class
elliot.recommender.knowledge_aware.kaHFM.kahfm.
KaHFM
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
Knowledge-aware Hybrid Factorization Machines
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: 0.05)
bias_regularization – Bias regularization (default: 0)
user_regularization – User regularization (default: 0.0025)
positive_item_regularization – regularization for positive (experienced) items (default: 0.0025)
negative_item_regularization – regularization for unknown items (default: 0.00025)
update_negative_item_factors – Boolean to update negative item factors (default: True)
update_users – Boolean to update user factors (default: True)
update_items – Boolean to update item factors (default: True)
update_bias – Boolean to update bias value (default: True)
To include the recommendation model, add it to the config file adopting the following pattern:
models: KaHFM: 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: 0.05 bias_regularization: 0 user_regularization: 0.0025 positive_item_regularization: 0.0025 negative_item_regularization: 0.00025 update_negative_item_factors: True update_users: True update_items: True update_bias: True
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property
name
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