朋友推荐
Last updated
Last updated
People You May Know: u2u推荐
场景/产品
Symmetrical Friendship: The system should recommend connections where both users are likely to accept the friendship.
目标
Network Growth: help users discover potential connections
User Retention: Improve user retention by enhancing the social experience
约束
Scale: the total number of users on the platform, and daily active users
Latency: Recommendations should be generated in real-time (e.g., < 200ms) to ensure a smooth user experience
average connections for one user
利用共同好友、位置、教育背景、工作经历判断可能认识
输入用户信息，输出和该用户最相似的k个用户作为推荐
User Profiles: Demographics, interests, and preferences
Social Graph: Existing connections and interactions (e.g., mutual friends, common groups)
Behavioral Data: Past interactions (e.g., profile views, connection requests)
User Features
Demographics: Age, location, education
Interests: Hobbies, favorite topics
Behavior: Frequency of interactions, types of interactions
Connection Features
Common Friends: Number of mutual friends. 共同好友个数
Shared Interests: Overlap in interests or groups
Interaction History: Past interactions between users (e.g., profile views, messages)
Context Features
Time of Day: Recommendations may vary based on the time of day
Device: Recommendations may differ based on the device used (e.g., mobile vs. desktop)
ranking
Collaborative Filtering: Recommend connections based on similar users' connections.
point-wise learning to rank
task 2 users as input, output the probability of forming a friend
graph based prediction
graph level prediction
predict if a chemical compound us an enzyme
node level prediction
predict if a specific user is a spammer
edge level prediction
predict if two users likely to connect
offline
precision, recall, and AUC-ROC
online
A/B Testing: Continuously test new models and features to measure their impact on engagement and network growth
Feedback Loop: Use user feedback to retrain and improve models
部分 batch serving
