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/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
/// Fakespot-specific logic
/// Score used to order Fakespot suggestions
///
/// FakespotScore contains several components, each in the range of [0, 1]
pub struct FakespotScore {
/// Did the query match the `keywords` field exactly?
keywords_score: f64,
/// How well did the query match the `product_type` field?
product_type_score: f64,
/// Fakespot score from the RS data, this reflects the average review, number of reviews,
/// Fakespot grade, etc.
fakespot_score: f64,
}
impl FakespotScore {
pub fn new(query: &str, keywords: String, product_type: String, fakespot_score: f64) -> Self {
let query = query.to_lowercase();
let query_terms = split_terms(&query);
Self {
keywords_score: calc_keywords_score(&query_terms, &keywords),
product_type_score: calc_product_type_score(&query_terms, &product_type),
fakespot_score,
}
}
/// Convert a FakespotScore into the value to use in `Sugggestion::Fakespot::score`
///
/// This converts FakespotScore into a single float that:
/// - Is > 0.3 so that Fakespot suggestions are preferred to AMP ones
/// - Reflects the Fakespot ordering:
/// - Suggestions with higher keywords_score are greater
/// - If keywords_score is tied, then suggestions with higher product_type_scores are greater
/// - If both are tied, then suggestions with higher fakespot_score are greater
pub fn as_suggest_score(&self) -> f64 {
0.30 + (0.01 * self.keywords_score)
+ (0.001 * self.product_type_score)
+ (0.0001 * self.fakespot_score)
}
}
/// Split a string containing terms into a list of individual terms, normalized to lowercase
fn split_terms(string: &str) -> Vec<&str> {
string.split_whitespace().collect()
}
fn calc_keywords_score(query_terms: &[&str], keywords: &str) -> f64 {
// Note: We can assume keywords is lower-case, since we do that during ingestion
let keyword_terms = split_terms(keywords);
if keyword_terms.is_empty() {
return 0.0;
}
if query_terms == keyword_terms {
1.0
} else {
0.0
}
}
fn calc_product_type_score(query_terms: &[&str], product_type: &str) -> f64 {
// Note: We can assume product_type is lower-case, since we do that during ingestion
let product_type_terms = split_terms(product_type);
if product_type_terms.is_empty() {
return 0.0;
}
let count = product_type_terms
.iter()
.filter(|t| query_terms.contains(t))
.count() as f64;
count / product_type_terms.len() as f64
}
#[cfg(test)]
mod tests {
use super::*;
struct KeywordsTestCase {
keywords: &'static str,
query: &'static str,
expected: f64,
}
impl KeywordsTestCase {
fn test(&self) {
let actual =
calc_keywords_score(&split_terms(&self.query.to_lowercase()), self.keywords);
assert_eq!(
actual, self.expected,
"keywords: {} query: {} expected: {} actual: {actual}",
self.keywords, self.query, self.expected,
);
}
}
#[test]
fn test_keywords_score() {
// Keyword score 1.0 on exact matches, 0.0 otherwise
KeywordsTestCase {
keywords: "apple",
query: "apple",
expected: 1.0,
}
.test();
KeywordsTestCase {
keywords: "apple",
query: "android",
expected: 0.0,
}
.test();
KeywordsTestCase {
keywords: "apple",
query: "apple phone",
expected: 0.0,
}
.test();
// Empty keywords should always score 0.0
KeywordsTestCase {
keywords: "",
query: "",
expected: 0.0,
}
.test();
KeywordsTestCase {
keywords: "",
query: "apple",
expected: 0.0,
}
.test();
// Matching should be case insensitive
KeywordsTestCase {
keywords: "apple",
query: "Apple",
expected: 1.0,
}
.test();
}
struct ProductTypeTestCase {
query: &'static str,
product_type: &'static str,
expected: f64,
}
impl ProductTypeTestCase {
fn test(&self) {
let actual = calc_product_type_score(
&split_terms(&self.query.to_lowercase()),
self.product_type,
);
assert_eq!(
actual, self.expected,
"product_type: {} query: {} expected: {} actual: {actual}",
self.product_type, self.query, self.expected,
);
}
}
#[test]
fn test_product_type_score() {
// Product type scores based on the percentage of terms in the product type that are also
// present in the query
ProductTypeTestCase {
product_type: "standing desk",
query: "standing desk",
expected: 1.0,
}
.test();
ProductTypeTestCase {
product_type: "standing desk",
query: "desk",
expected: 0.5,
}
.test();
ProductTypeTestCase {
product_type: "standing desk",
query: "desk desk desk",
expected: 0.5,
}
.test();
ProductTypeTestCase {
product_type: "standing desk",
query: "standing",
expected: 0.5,
}
.test();
ProductTypeTestCase {
product_type: "standing desk",
query: "phone",
expected: 0.0,
}
.test();
// Extra terms in the query are ignored
ProductTypeTestCase {
product_type: "standing desk",
query: "standing desk for my office",
expected: 1.0,
}
.test();
// Empty product_type should always score 0.0
ProductTypeTestCase {
product_type: "",
query: "",
expected: 0.0,
}
.test();
// Matching should be case insensitive
ProductTypeTestCase {
product_type: "desk",
query: "Desk",
expected: 1.0,
}
.test();
// Extra spaces are ignored
ProductTypeTestCase {
product_type: "desk",
query: " desk ",
expected: 1.0,
}
.test();
}
}