KGCN

Reference:

Hongwei Wang et al. “Knowledge graph convolution networks for recommender systems.” in WWW 2019.

Reference code:

https://github.com/hwwang55/KGCN

class recbole.model.knowledge_aware_recommender.kgcn.KGCN(config, dataset)

Bases: KnowledgeRecommender

KGCN is a knowledge-based recommendation model that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we treat KG as an undirected graph and sample from the neighbors for each entity in the KG as their receptive field, then combine neighborhood information with bias when calculating the representation of a given entity.

aggregate(user_embeddings, entities, relations)

For each item, aggregate the entity representation and its neighborhood representation into a single vector.

Parameters:

• user_embeddings (torch.FloatTensor) – The embeddings of users, shape: [batch_size, embedding_size]

• entities (list) – entities is a list of i-iter (i = 0, 1, …, n_iter) neighbors for the batch of items. dimensions of entities: {[batch_size, 1], [batch_size, n_neighbor], [batch_size, n_neighbor^2], …, [batch_size, n_neighbor^n_iter]}

• relations (list) – relations is a list of i-iter (i = 0, 1, …, n_iter) corresponding relations for entities. relations have the same shape as entities.

Returns:

The embeddings of items, shape: [batch_size, embedding_size]

Return type:

item_embeddings(torch.FloatTensor)

calculate_loss(interaction)

Calculate the training loss for a batch data.

Parameters:

interaction (Interaction) – Interaction class of the batch.

Returns:

Training loss, shape: []

Return type:

torch.Tensor

construct_adj(kg_graph)

Get neighbors and corresponding relations for each entity in the KG.

Parameters:

kg_graph (scipy.sparse.coo_matrix) – an undirected graph

Returns:

• adj_entity(torch.LongTensor): each line stores the sampled neighbor entities for a given entity, shape: [n_entities, neighbor_sample_size]

• adj_relation(torch.LongTensor): each line stores the corresponding sampled neighbor relations, shape: [n_entities, neighbor_sample_size]

Return type:

tuple

forward(user, item)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

full_sort_predict(interaction)

full sort prediction function. Given users, calculate the scores between users and all candidate items.

Parameters:

interaction (Interaction) – Interaction class of the batch.

Returns:

Predicted scores for given users and all candidate items, shape: [n_batch_users * n_candidate_items]

Return type:

torch.Tensor

get_neighbors(items)

Get neighbors and corresponding relations for each entity in items from adj_entity and adj_relation.

Parameters:

items (torch.LongTensor) – The input tensor that contains item’s id, shape: [batch_size, ]

Returns:

• entities(list): Entities is a list of i-iter (i = 0, 1, …, n_iter) neighbors for the batch of items. dimensions of entities: {[batch_size, 1], [batch_size, n_neighbor], [batch_size, n_neighbor^2], …, [batch_size, n_neighbor^n_iter]}

• relations(list): Relations is a list of i-iter (i = 0, 1, …, n_iter) corresponding relations for entities. Relations have the same shape as entities.

Return type:

tuple

input_type = 2

mix_neighbor_vectors(neighbor_vectors, neighbor_relations, user_embeddings)

Mix neighbor vectors on user-specific graph.

Parameters:

• neighbor_vectors (torch.FloatTensor) – The embeddings of neighbor entities(items), shape: [batch_size, -1, neighbor_sample_size, embedding_size]

• neighbor_relations (torch.FloatTensor) – The embeddings of neighbor relations, shape: [batch_size, -1, neighbor_sample_size, embedding_size]

• user_embeddings (torch.FloatTensor) – The embeddings of users, shape: [batch_size, embedding_size]

Returns:

The neighbors aggregated embeddings, shape: [batch_size, -1, embedding_size]

Return type:

neighbors_aggregated(torch.FloatTensor)

predict(interaction)

Predict the scores between users and items.

Parameters:

interaction (Interaction) – Interaction class of the batch.

Returns:

Predicted scores for given users and items, shape: [batch_size]

Return type:

torch.Tensor

training: bool