I am making a recommender system. For similar queries I want to give similar recommendations.
I was following the example from here: https://www.tensorflow.org/recommenders/examples/featurization?hl=en
User creates a query. He can choose the desired location on the map that's why query has longitude and latitude in its features. I want to add longitude and latitude in the Query model for the recommender algorithm.
Here is the Query model. It already takes the tokenized text features:
class QueryModel(tf.keras.Model):
def __init__(self):
super().__init__()
max_tokens = 10_000
self.query_features_vectorizer = tf.keras.layers.TextVectorization(
max_tokens=max_tokens)
self.query_features_embedding = tf.keras.Sequential([
self.query_features_vectorizer,
tf.keras.layers.Embedding(max_tokens, 64, mask_zero=True),
tf.keras.layers.GlobalAveragePooling1D(),
])
self.query_features_vectorizer.adapt(query_features)
def call(self, inputs):
# Take the input dictionary, pass it through each input layer,
# and concatenate the result.
return tf.concat([
self.query_features_embedding(inputs["query_features"]),
], axis=1)
I pass query model into this ranking model that has a task to give ratings to each query-candidate pair:
class RatingsModel(tfrs.models.Model):
def __init__(self):
super().__init__()
# query and warehouse models
self.query_model = tf.keras.Sequential([
QueryModel(),
tf.keras.layers.Dense(16)
])
self.candidate_model = tf.keras.Sequential([
WarehouseModel(),
tf.keras.layers.Dense(16)
])
# A small model to take in query and warehouse embeddings and predict ratings.
# We can make this as complicated as we want as long as we output a scalar
# as our prediction.
self.rating_model = tf.keras.Sequential([
tf.keras.layers.Dense(16, activation="linear"),
tf.keras.layers.Dense(8, activation="tanh"),
tf.keras.layers.Dense(1, activation="linear"),
])
self.task = tfrs.tasks.Ranking(
loss=tf.keras.losses.MeanSquaredError(),
metrics=[tf.keras.metrics.RootMeanSquaredError("RMSE")]
)
def call(self, features):
query_embeddings = self.query_model({
"query_features": features["query_features"],
})
warehouse_embeddings = self.candidate_model({
"warehouse_id": features["warehouse_id"],
})
return (
self.rating_model(
tf.concat([query_embeddings, warehouse_embeddings], axis=1)
),
)
def compute_loss(self, features, training=False):
labels = features.pop("similarity")
rating_predictions = self(features)
# We compute the loss for each task.
rating_loss = self.task(
labels=labels,
predictions=rating_predictions,
)
return rating_loss
The algorithm predicts the rating for the most relevant candidates for the query.
My question is: how can I take the longitude and latitude into account, what is the general approach?
