Twitter Recommendation Algorithm

Please note we have force-pushed a new initial commit in order to remove some publicly-available Twitter user information. Note that this process may be required in the future.

navi/thrift_bpr_adapter/thrift/src/decoder.rs

@@ -0,0 +1,78 @@
+
+// A feature value can be one of these
+enum FeatureVal {
+  Empty,
+  U8Vector(Vec<u8>),
+  FloatVector(Vec<f32>),
+}
+
+// A Feture has a name and a value
+// The name for now is 'id' of type string
+// Eventually this needs to be flexible - example to accomodate feature-id
+struct Feature {
+  id: String,
+  val: FeatureVal,
+}
+
+impl Feature {
+  fn new() -> Feature {
+    Feature {
+      id: String::new(),
+      val: FeatureVal::Empty
+    }
+  }
+}
+
+// A single inference record will have multiple features
+struct Record {
+  fields: Vec<Feature>,
+}
+
+impl Record {
+  fn new() -> Record {
+    Record { fields: vec![] }
+  }
+}
+
+// This is the main API used by external components
+// Given a serialized input, decode it into Records
+fn decode(input: Vec<u8>) -> Vec<Record> {
+  // For helping define the interface
+  vec![get_random_record(), get_random_record()]
+}
+
+// Used for testing the API, will be eventually removed
+fn get_random_record() -> Record {
+  let mut record: Record = Record::new();
+
+  let f1: Feature = Feature {
+    id: String::from("continuous_features"),
+    val: FeatureVal::FloatVector(vec![1.0f32; 2134]),
+  };
+
+  record.fields.push(f1);
+
+  let f2: Feature = Feature {
+    id: String::from("user_embedding"),
+    val: FeatureVal::FloatVector(vec![2.0f32; 200]),
+  };
+
+  record.fields.push(f2);
+
+  let f3: Feature = Feature {
+    id: String::from("author_embedding"),
+    val: FeatureVal::FloatVector(vec![3.0f32; 200]),
+  };
+
+  record.fields.push(f3);
+
+  let f4: Feature = Feature {
+    id: String::from("binary_features"),
+    val: FeatureVal::U8Vector(vec![4u8; 43]),
+  };
+
+  record.fields.push(f4);
+
+  record
+}
+