VladLujerdeanu
commented
2 years ago Never mind, I think I got it in the end. If anyone needs it:
class RankingModel(tfrs.Model):
def __init__(self, query_model, candidate_model):
super().__init__()
self.query_model = query_model
self.candidate_model = candidate_model
self.ratings = tf.keras.Sequential([
# Learn multiple dense layers.
tf.keras.layers.Dense(256, activation="relu"),
tf.keras.layers.Dense(64, activation="relu"),
# Make rating predictions in the final layer.
tf.keras.layers.Dense(1)
])
self.task = tfrs.tasks.Ranking(
loss=tf.keras.losses.MeanSquaredError(),
metrics=[
tf.keras.metrics.RootMeanSquaredError()
]
)
def call(self, features):
watch_history = features["context_movie_id"]
watch_next_label = features["label_movie_id"]
query_embedding = self.query_model(watch_history)
candidate_embedding = self.candidate_model(watch_next_label)
return self.ratings(tf.concat([query_embedding, candidate_embedding], axis=1))
def compute_loss(self, features, training=False):
ratings = features.pop("context_movie_rating")
rating_predictions = self(features)
return self.task(labels=ratings, predictions=rating_predictions)
patrickorlando
datasciyj
Hello! I am trying to build a recommendation system using tfrs. I managed to build a retrieval model using the Retrieval with Sequential Model tutorial, but I don't know where to go from here. I've tried to build the model ranking model based on the retrieval model but I failed. Now, sorry if it's a dumb question, I'm still learning but, can I build a model of Ranking using the same GRU layer? If yes, can you point me to an example of how should I build such a model or help me with a model that for the Movielens dataset like in the tutorial?