elliot.recommender.NN.attribute_user_knn package

Submodules

elliot.recommender.NN.attribute_user_knn.attribute_user_knn module

Module description:

class elliot.recommender.NN.attribute_user_knn.attribute_user_knn.AttributeUserKNN(data, config, params, *args, **kwargs)

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

Attribute User-kNN proposed in MyMediaLite Recommender System Library

For further details, please refer to the paper

Parameters

• neighbors – Number of item neighbors

• similarity – Similarity function

• profile – Profile type (‘binary’, ‘tfidf’)

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

models:
  AttributeUserKNN:
    meta:
      save_recs: True
    neighbors: 40
    similarity: cosine
    profile: binary

build_feature_sparse()

build_feature_sparse_values()

compute_binary_profile(user_items_dict: Dict)

get_recommendations(k: int = 100)

property name

restore_weights()

train()

elliot.recommender.NN.attribute_user_knn.attribute_user_knn_similarity module

class elliot.recommender.NN.attribute_user_knn.attribute_user_knn_similarity.Similarity(data, attribute_matrix, num_neighbors, similarity)

Bases: object

Simple kNN class

compute_neighbors()

get_model_state()

get_transactions()

get_user_neighbors(item)

get_user_recs(u, k)

initialize()

This function initialize the data model

process_similarity(similarity)

static score_item(neighs, user_neighs_items)

set_model_state(saving_dict)

elliot.recommender.NN.attribute_user_knn.tfidf_utils module

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

Bases: object

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

tfidf()

Module contents