X Algorithm Engagement Signals

The X recommendation algorithm tracks 18 engagement action types plus 1 continuous metric. These are predicted by the Phoenix ML model and used to calculate weighted scores.

PhoenixScores Struct

Defined in

home-mixer/candidate_pipeline/candidate.rs

rust

pub struct PhoenixScores {
    // Positive engagement signals
    pub favorite_score: Option<f64>,
    pub reply_score: Option<f64>,
    pub retweet_score: Option<f64>,
    pub quote_score: Option<f64>,
    pub share_score: Option<f64>,
    pub share_via_dm_score: Option<f64>,
    pub share_via_copy_link_score: Option<f64>,
    pub follow_author_score: Option<f64>,

    // Engagement metrics
    pub photo_expand_score: Option<f64>,
    pub click_score: Option<f64>,
    pub profile_click_score: Option<f64>,
    pub vqv_score: Option<f64>,              // Video Quality View
    pub dwell_score: Option<f64>,
    pub quoted_click_score: Option<f64>,

    // Negative signals
    pub not_interested_score: Option<f64>,
    pub block_author_score: Option<f64>,
    pub mute_author_score: Option<f64>,
    pub report_score: Option<f64>,

    // Continuous actions
    pub dwell_time: Option<f64>,
}

Action Types by Category

Positive Engagement (High Value)

Action	Proto Name	Description
Favorite	`ServerTweetFav`	User likes the post
Reply	`ServerTweetReply`	User replies to the post
Retweet	`ServerTweetRetweet`	User reposts without comment
Quote	`ServerTweetQuote`	User reposts with their own comment
Follow Author	`ClientTweetFollowAuthor`	User follows the post's author

Sharing Actions

Action	Proto Name	Description
Share	`ClientTweetShare`	Generic share action
Share via DM	`ClientTweetClickSendViaDirectMessage`	User shares via direct message
Share via Copy Link	`ClientTweetShareViaCopyLink`	User copies link to share externally

Engagement Metrics

Action	Proto Name	Description
Photo Expand	`ClientTweetPhotoExpand`	User expands photo to view
Click	`ClientTweetClick`	User clicks on the post
Profile Click	`ClientTweetClickProfile`	User clicks author's profile
VQV	`ClientTweetVideoQualityView`	Video Quality View - user watches video for meaningful duration
Dwell	`ClientTweetRecapDwelled`	User dwells (pauses) on the post
Quoted Click	`ClientQuotedTweetClick`	User clicks on a quoted post

Negative Signals

Action	Proto Name	Description
Not Interested	`ClientTweetNotInterestedIn`	User marks as not interested
Block Author	`ClientTweetBlockAuthor`	User blocks the author
Mute Author	`ClientTweetMuteAuthor`	User mutes the author
Report	`ClientTweetReport`	User reports the post

Continuous Actions

Action	Proto Name	Description
Dwell Time	`DwellTime`	Continuous value: seconds spent viewing post

How Scores Are Obtained

The

PhoenixScorer

(

home-mixer/scorers/phoenix_scorer.rs

) calls the Phoenix prediction service:

Input: User history + candidate posts
Output: Log probabilities for each action type per candidate
Conversion:
```
probability = exp(log_prob)
```

rust

fn extract_phoenix_scores(&self, p: &ActionPredictions) -> PhoenixScores {
    PhoenixScores {
        favorite_score: p.get(ActionName::ServerTweetFav),
        reply_score: p.get(ActionName::ServerTweetReply),
        retweet_score: p.get(ActionName::ServerTweetRetweet),
        // ... maps each action to its probability
    }
}

Signal Interpretation

Scores are probabilities (0.0 to 1.0): P(user takes action | user sees post)
Higher = more likely: A
```
favorite_score
```
of 0.15 means 15% predicted chance of like
Negative signals have negative weights: High
```
report_score
```
reduces overall ranking
VQV requires minimum video duration: Only applies to videos >
```
MIN_VIDEO_DURATION_MS
```

Related Skills

```
/x-algo-scoring
```
- How these signals are combined into a weighted score
```
/x-algo-ml
```
- How Phoenix model predicts these probabilities

x-algo-engagement

NPX Install

Tags

SKILL.md Content