model.py

def build_model(n_users, n_items, emb_dim = 30):
    '''
        Define the Keras Model for training 
        
        Parameters
        ----------
        
            n_users : int
                        number of users
            
            n_items : int
                        number of items
                        
            user_features : list of str
                                list of categorical features (columns of df_users)
            
            item_features : list of str
                                list of categorical features (columns of df_items)
            
            emb_dim : int
                        dimension of the embedding space

    '''
    n_user_features = 3
    n_item_features = 18

    ### Input Layers

    user_input = Input((n_user_features,), name='user_input')
    positive_item_input = Input((n_item_features,), name='pos_item_input')
    negative_item_input = Input((n_item_features,), name='neg_item_input')

    inputs = [user_input, positive_item_input, negative_item_input]

    ### Embedding Layers

    user_emb = Embedding(n_users, emb_dim, input_length=n_user_features, name='user_emb')
    # Positive and negative items will share the same embedding
    item_emb = Embedding(n_items, emb_dim, input_length=n_item_features, name='item_emb')
    # Layer to convert embedding vectors in the same dimensional vectors
    vec_conv32 = Dense(32, name = 'dense_vec32', activation = 'relu')
    vec_conv = Dense(emb_dim, name = 'dense_vec', activation = 'softmax')
    

    # Anchor
    a = Flatten(name = 'flatten_usr_emb')(user_emb(user_input))
    a = Dense(emb_dim, name = 'dense_user', activation = 'softmax')(a)
    
    # Positive
    p = Flatten(name = 'flatten_pos_emb')(item_emb(positive_item_input))
    p = vec_conv32(p)
    p = vec_conv(p)

    # Negative
    n = Flatten(name = 'flatten_neg_emb')(item_emb(negative_item_input))
    n = vec_conv32(n)
    n = vec_conv(n)

    # Score layers
    p_rec_score = ScoreLayer(name='pos_recommendation_score')([a, p])
    n_rec_score = ScoreLayer(name='neg_recommendation_score')([a, n])
    
    # TripletLoss Layer
    loss_layer = TripletLossLayer(name='triplet_loss_layer')([a, p, n])
    
    # Connect the inputs with the outputs
    network_train = Model(inputs=inputs, outputs=loss_layer, name = 'training_model')

    network_predict = Model(inputs=inputs[:-1], outputs=p_rec_score, name = 'inference_model')
    
    # return the model
    return network_train, network_predict