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深度学习框架

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Building deep retrieval models

tensorflow 推荐系统深度学习 NLP 服务

In the featurization tutorial we incorporated multiple features into our models, but the models consist of only an embedding layer. We can add more dense layers to our models to increase their expressive power. In general, deeper models are capable of learning more complex patterns than shallower models. For example, our user model incorporates user ids and timestamps to model user preferences at a point in time. A shallow model (say, a single embedding layer) may only be able to learn the simplest relationships between those features and movies: a given movie is most popular around the time of its release, and a given user generally prefers horror movies to comedies. To capture more complex relationships, such as user preferences evolving over time, we may need a deeper model with multiple stacked dense layers.

2021-07-30

3200

Taking advantage of context features

tensorflow 深度学习推荐系统 NLP 服务

In the featurization tutorial we incorporated multiple features beyond just user and movie identifiers into our models, but we haven't explored whether those features improve model accuracy.

2021-07-30

2110

Using side features: feature preprocessing

tensorflow keras 推荐系统深度学习 NLP 服务

One of the great advantages of using a deep learning framework to build recommender models is the freedom to build rich, flexible feature representations.

2021-07-30

3960

TensorFlow Recommenders: Quickstart

tensorflow 推荐系统 keras 深度学习

In this tutorial, we build a simple matrix factorization model using the MovieLens 100K dataset with TFRS. We can use this model to recommend movies for a given user.

2021-07-30

3730

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