x-algo-engagement

name x-algo-engagement description Reference for X algorithm engagement types and signals. Use when analyzing engagement metrics, action predictions, or understanding what signals the algorithm tracks. 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 : 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) 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