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tensorflow/lite/g3doc/examples/recommendation/overview.md

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+# Recommendation
+
+<table class="tfo-notebook-buttons" align="left">
+  <td>
+    <a target="_blank" href="https://www.tensorflow.org/lite/examples/recommendation/overview"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
+  </td>
+  {% dynamic if request.tld != 'cn' %}<td>
+    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/examples/blob/master/lite/examples/recommendation/ml/ondevice_recommendation.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
+  </td>{% dynamic endif %}
+  <td>
+    <a target="_blank" href="https://github.com/tensorflow/examples/blob/master/lite/examples/recommendation/ml/ondevice_recommendation.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
+  </td>
+</table>
+
+Personalized recommendations are widely used for a variety of use cases on
+mobile devices, such as media content retrieval, shopping product suggestion,
+and next app recommendation. If you are interested in providing personalized
+recommendations in your application while respecting user privacy, we recommend
+exploring the following example and toolkit.
+
+Note: To customize a model, try
+[TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker).
+
+## Get started
+
+<img src="images/screenshot.gif" class="attempt-right" style="max-width: 300px">
+
+We provide a TensorFlow Lite sample application that demonstrates how to
+recommend relevant items to users on Android.
+
+<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/android">Android
+example</a>
+
+If you are using a platform other than Android, or you are already familiar with
+the TensorFlow Lite APIs, you can download our starter recommendation model.
+
+<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/recommendation/20200720/recommendation.tar.gz">Download
+starter model</a>
+
+We also provide training script in Github to train your own model in a
+configurable way.
+
+<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/ml">Training
+code</a>
+
+## Understand the model architecture
+
+We leverage a dual-encoder model architecture, with context-encoder to encode
+sequential user history and label-encoder to encode predicted recommendation
+candidate. Similarity between context and label encodings is used to represent
+the likelihood that the predicted candidate meets the user's needs.
+
+Three different sequential user history encoding techniques are provided with
+this code base:
+
+*   Bag-of-words encoder (BOW): averaging user activities' embeddings without
+    considering context order.
+*   Convolutional neural network encoder (CNN): applying multiple layers of
+    convolutional neural networks to generate context encoding.
+*   Recurrent neural network encoder (RNN): applying recurrent neural network to
+    encode context sequence.
+
+To model each user activity, we could use the ID of the activity item (ID-based)
+, or multiple features of the item (feature-based), or a combination of both.
+The feature-based model utilizing multiple features to collectively encode
+users’ behavior. With this code base, you could create either ID-based or
+feature-based models in a configurable way.
+
+After training, a TensorFlow Lite model will be exported which can directly
+provide top-K predictions among the recommendation candidates.
+
+## Use your training data
+
+In addition to the trained model, we provide an open-sourced
+[toolkit in GitHub](https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/ml)
+to train models with your own data. You can follow this tutorial to learn how to
+use the toolkit and deploy trained models in your own mobile applications.
+
+Please follow this
+[tutorial](https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/ml/ondevice_recommendation.ipynb)
+to apply the same technique used here to train a recommendation model using your
+own datasets.
+
+## Examples
+
+As examples, we trained recommendation models with both ID-based and
+feature-based approaches. The ID-based model takes only the movie IDs as input,
+and the feature-based model takes both movie IDs and movie genre IDs as inputs.
+Please find the following inputs and outputs examples.
+
+Inputs
+
+*   Context movie IDs:
+
+    *   The Lion King (ID: 362)
+    *   Toy Story (ID: 1)
+    *   (and more)
+
+*   Context movie genre IDs:
+
+    *   Animation (ID: 15)
+    *   Children's (ID: 9)
+    *   Musical (ID: 13)
+    *   Animation (ID: 15)
+    *   Children's (ID: 9)
+    *   Comedy (ID: 2)
+    *   (and more)
+
+Outputs:
+
+*   Recommended movie IDs:
+    *   Toy Story 2 (ID: 3114)
+    *   (and more)
+
+Note: The pretrained model is built based on
+[MovieLens](https://grouplens.org/datasets/movielens/1m/) dataset for research
+purpose.
+
+## Performance benchmarks
+
+Performance benchmark numbers are generated with the tool
+[described here](https://www.tensorflow.org/lite/performance/benchmarks).
+
+<table>
+  <thead>
+    <tr>
+      <th>Model Name</th>
+      <th>Model Size </th>
+      <th>Device </th>
+      <th>CPU</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <tr>
+        <td rowspan = 3>
+          <a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/recommendation/20200720/model.tar.gz">recommendation (movie ID as input)</a>
+        </td>
+        <td rowspan = 3>
+          0.52 Mb
+        </td>
+        <td>Pixel 3</td>
+        <td>0.09ms*</td>
+      </tr>
+       <tr>
+         <td>Pixel 4 </td>
+        <td>0.05ms*</td>
+      </tr>
+    </tr>
+    <tr>
+      <tr>
+        <td rowspan = 3>
+          <a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/recommendation/20210317/recommendation_cnn_i10i32o100.tflite">recommendation (movie ID and movie genre as inputs)</a>
+        </td>
+        <td rowspan = 3>
+          1.3 Mb
+        </td>
+        <td>Pixel 3</td>
+        <td>0.13ms*</td>
+      </tr>
+       <tr>
+         <td>Pixel 4 </td>
+        <td>0.06ms*</td>
+      </tr>
+    </tr>
+  </tbody>
+</table>
+
+\* 4 threads used.
+
+## Use your training data
+
+In addition to the trained model, we provide an open-sourced
+[toolkit in GitHub](https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/ml)
+to train models with your own data. You can follow this tutorial to learn how to
+use the toolkit and deploy trained models in your own mobile applications.
+
+Please follow this
+[tutorial](https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/ml/ondevice_recommendation.ipynb)
+to apply the same technique used here to train a recommendation model using your
+own datasets.
+
+## Tips for model customization with your data
+
+The pretrained model integrated in this demo application is trained with
+[MovieLens](https://grouplens.org/datasets/movielens/1m/) dataset, you may want
+to modify model configuration based on your own data, such as vocab size,
+embedding dims and input context length. Here are a few tips:
+
+*   Input context length: The best input context length varies with datasets. We
+    suggest selecting input context length based on how much label events are
+    correlated with long-term interests vs short-term context.
+
+*   Encoder type selection: we suggest selecting encoder type based on input
+    context length. Bag-of-words encoder works well for short input context
+    length (e.g. <10), CNN and RNN encoders bring in more summarization ability
+    for long input context length.
+
+*   Using underlying features to represent items or user activities could
+    improve model performance, better accommodate fresh items, possibly down
+    scale embedding spaces hence reduce memory consumption and more on-device
+    friendly.