推荐系统

从订餐到视频点播和音频在线播放，再到时尚内容，推荐系统可为当今一些极受欢迎的应用提供助力。探索如何利用 TensorFlow 生态系统中的开源库和工具构建可用于生产环境的推荐系统。

Recommendation systems increase user engagement within your app and elevate user experience by providing the most desirable content. Modern recommenders are complex systems that are often broken down into multiple stages to achieve low latency in production. Through the retrieval, ranking, and potentially post-ranking stages, irrelevant items are gradually filtered out from a large pool of candidates and a list of options that users are the most likely to interact with are finally presented.

Start building with TensorFlow Recommenders, an easy-to-use framework that facilitates the full workflow of building a recommender system from data preparation to deployment.

TensorFlow Recommenders 使用入门

完成模型训练后，您可以将模型部署到生产环境中，以向最终用户提供推荐内容。TensorFlow Serving 可将模型投入生产环境，实现高性能推断。该平台旨在最大限度提高机器学习模型的吞吐量，并且能够为需要分布式服务的大型推荐模型提供支持。

TensorFlow Serving 使用入门

# Deploy the retrieval model with TensorFlow Serving
docker run -t --rm -p 8501:8501 \
  -v "RETRIEVAL/MODEL/PATH:/models/retrieval" \
  -e MODEL_NAME=retrieval tensorflow/serving &

# Retrieve top movies that user 42 may like
curl -X POST -H "Content-Type: application/json" \
  -d '{"instances":["42"]}'  \
  http://localhost:8501/v1/models/retrieval:predict

# Output
# {
#    "predictions":[
#       {
#          "output_1": [2.032, 1.969, 1.813],
#          "output_2": ["movie1”, “movie2”, “movie3”]
#       }
#    ]
# }

# Deploy the ranking model with TensorFlow Serving
docker run -t --rm -p 8501:8501 \
  -v "RANKING/MODEL/PATH:/models/ranking" \
  -e MODEL_NAME=ranking tensorflow/serving &

# Get the prediction score for user 42 and movie 3
curl -X POST -H "Content-Type: application/json" \
  -d '{"instances":[{"user_id":"42", "movie_title":"movie3"}]}' \
  http://localhost:8501/v1/models/ranking:predict

# Output:
# {"predictions": [[3.66357923]]}

code_blocks

了解如何使用 TensorFlow 构建和部署全栈推荐系统

Google ScaNN

ScaNN 是一个用于进行大规模向量相似度搜索的库。该库利用先进的 ANN 技术（例如非对称哈希处理和各向异性量化）来加速检索热门候选项。

GitHub 博文教程

TensorFlow Ranking

TensorFlow Ranking 是一个用于开发可扩容的神经 LTR 模型的库。该库提供了额外的功能来对候选推荐项进行排序，以便最大限度发挥排序实用程序的效用。

GitHub 博文教程