Intro to TensorFlow Recommenders (Building recommendation systems with TensorFlow)

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  • Опубликовано: 8 ноя 2024

Комментарии • 22

  • @sohaibarif2835
    @sohaibarif2835 2 года назад +10

    I would really like more explanation of the factorized top k Brute Force and SCANN methods. The written docs don't say much, and I can sort of understand based on context what it is doing but would love a more detailed explanation on those two.

    • @windmaple
      @windmaple 2 года назад +3

      Interesting. I can prob. make a video to better explain approximate neighbor search (which is what ScaNN does) so that it makes more sense.

    • @massiveblackwood
      @massiveblackwood 2 года назад +1

      @@windmaple why use id as embeddings instead of genre and gender? It make more sense?

  • @agung_laksana
    @agung_laksana 3 года назад +5

    Please make Firebase Extension for this recommendation system 🤣

  • @jacobcohen76
    @jacobcohen76 Год назад +1

    I noticed in this video, they are using the userId as an input to the model to generate the vector embeddings for what movies that user likes. What happens when we add more users with new userIds? Do we have to retrain the model every time?

    • @MrAmgadHasan
      @MrAmgadHasan Год назад +2

      Yes. One other solution if you don't want to retrain the model every time is to create an embedding for ids that are not in the training set. This embedding will be used foe new users. It is similar to out of vocab tokens in nlp.

    • @olatheog
      @olatheog Год назад

      @@MrAmgadHasan are you here bro? I keep getting 'error:Cannot batch tensors with different shapes in component 0. First element had shape [1] and element 1 had shape [2]. [Op:IteratorGetNext]' whenever I try to this at 5:35 --> movie_titles = movies.batch(1_000).map(lambda x: x['movie_title'])
      user_ids = ratings.batch(1_000_000).map(lambda x: x["user_id"])
      unique_movie_titles = np.unique(np.concatenate(list(movie_titles)))
      unique_user_ids = np.unique(np.concatenate(list(user_ids)))
      unique_movie_titles[:4]
      why? help me out here please

  • @tiemoko.s
    @tiemoko.s 3 года назад +3

    Can I a create recommender system model for my streaming mobile app ? If yes, how?

    • @SuiGio
      @SuiGio 3 года назад +24

      Start coding.

    • @SS-cz2de
      @SS-cz2de 2 года назад +1

      Tensorflow Lite will help you here.

  • @guitar300k
    @guitar300k 2 года назад +3

    This is tough

  • @spkt1001
    @spkt1001 3 года назад +2

    at 9:31 (ruclips.net/video/jz0-satrmrA/видео.html) what is the colon in __ini__ function for? For example the part with "self.task: tf.keras.layers.Layer = task"

    • @lebesgue-integral
      @lebesgue-integral 2 года назад

      This is just a type annotation. It means that he's passing the type of each argument to easy understanding the matter of each argument. It's optional, so you could write directly "self.task = task" instead of "self.task: tf.keras.layers.Layer = task"

    • @Davidmine97
      @Davidmine97 2 года назад

      @@lebesgue-integral ol

    • @Davidmine97
      @Davidmine97 2 года назад

      O

  • @gauravbhasin2625
    @gauravbhasin2625 5 месяцев назад

    wow just wow

  • @xnxbxs_zx
    @xnxbxs_zx 3 года назад

    Thank you 🙌

  • @donfeto7636
    @donfeto7636 9 месяцев назад +2

    Not Recommnded videos to watch,

  • @massiveblackwood
    @massiveblackwood 2 года назад +1

    why use id random number as embedding? why using identity matrix as label? pls i need further explanation about. tf.eye as label for loss function will be helpful thanks!

  • @dinugakasun5218
    @dinugakasun5218 3 года назад

    Thank you !