elliot.recommender.content_based.VSM package

Submodules

elliot.recommender.content_based.VSM.tfidf_utils module

class elliot.recommender.content_based.VSM.tfidf_utils.TFIDF(map: Dict[int, List[int]])

Bases: object

get_profiles(ratings: Dict[int, Dict[int, float]])

tfidf()

elliot.recommender.content_based.VSM.vector_space_model module

Module description:

class elliot.recommender.content_based.VSM.vector_space_model.VSM(data, config, params, *args, **kwargs)

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Vector Space Model

For further details, please refer to the paper and the paper

Parameters

• similarity – Similarity metric

• user_profile –

• item_profile –

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  VSM:
    meta:
      save_recs: True
    similarity: cosine
    user_profile: binary
    item_profile: binary

build_feature_sparse(feature_dict, num_entities)

build_feature_sparse_values(feature_dict, num_entities)

compute_binary_profile(user_items_dict: Dict)

get_recommendations(k: int = 10)

get_single_recommendation(mask, k, *args)

property name

train()

elliot.recommender.content_based.VSM.vector_space_model_similarity module

class elliot.recommender.content_based.VSM.vector_space_model_similarity.Similarity(data, user_profile_matrix, item_attribute_matrix, similarity)

Bases: object

Simple VSM class

get_model_state()

get_user_recs(u, mask, k)

initialize()

This function initialize the data model

load_weights(path)

process_similarity(similarity)

save_weights(path)

set_model_state(saving_dict)

Module contents