The Tab Ranker is an experimental model used to predict tab reactivation. We are setting up experiments to use this model as a factor in deciding which tabs to discard when the system is low on memory.
The input to the model is a list of metrics about a background tab, such as whether it is pinned, how long it has been in the background, and how many events it has received. The output is a floating point value, where a higher number suggests the tab is more likely to be reactivated than a lower number. The range of possible outputs is dependent on the model; what matters is the relative ordering of scores, not the precise value of a score.
The metrics used for inference are the same metrics logged to UKM by TabMetricsLogger. Using these metrics, we trained a DNN model. The inferencing code in native_inference.cc consists of the model weights generated by TensorFlow and basic operations to execute the model over an example.
See [native_inference.md] for details on how the tf.native code is cleaned up for Chromium.
Examples are provided to the model as an array of floats, with each element corresponding to a feature. For scalar metrics like WasRecentlyActive(), this feature is simply a 0 or 1.
A metric with multiple possible values, like an enum or bucketized number, is broken into a number of features, each corresponding to one of its possible values. For example, a value of 7 for the PageTransitionCoreType metric is represented as a 1 in the PageTransitionCoreType_7 feature. See https://en.wikipedia.org/wiki/One-hot.
The AssistRanker handles this example preprocessing based on the configuration provided in example_preprocessor_config.pb. This can be examined with the print_example_preprocessor_config.py utility:
./components/assist_ranker/print_example_preprocessor_config.py \ out/Release \ chrome/browser/resource_coordinator/tab_ranker/example_preprocessor_config.pb
Use TabActivityWatcher::CalculateReactivationScore() to get a score from a WebContents.
Internally, TabScorePredictor::ScoreTab is the interface to the model. The caller needs to track a number of metrics about the tab and its window. These metrics must be calculated in the same way they were logged, as otherwise the results will not be valid.