twitter-the-algorithm/src/scala/com/twitter/timelines/prediction/features
twitter-team 197bf2c563 Open-sourcing Timelines Aggregation Framework
Open sourcing Aggregation Framework, a config-driven Summingbird based framework for generating real-time and batch aggregate features to be consumed by ML models.
2023-04-28 14:17:02 -05:00
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client_log_event Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
common Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
engagement_features Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
escherbird Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
followsource Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
itl Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
list_features Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
p_home_latest Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
ppmi Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
real_graph Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
recap Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
request_context Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
simcluster Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
socialproof Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
time_features Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
two_hop_features Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
user_health Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00
README.md Open-sourcing Timelines Aggregation Framework 2023-04-28 14:17:02 -05:00

README.md

Prediction Features

This directory contains a collection of Features (com.twitter.ml.api.Feature) which are definitions of feature names and datatypes which allow the features to be efficiently processed and passed to the different ranking models. By predefining the features with their names and datatypes, when features are being generated, scribed or used to score they can be identified with only a hash of their name.

Not all of these features are used in the model, many are experimental or deprecated.