Abstract: Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model—that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system—with the more lightweight, single objective model—that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

Abstract: Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model—that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system—with the more lightweight, single objective model—that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

Abstract: A feature importance score for a target machine learning feature of a target machine learning model used in a multistage feed ranking system for scoring feed items is supplemented with a feature computing resource cost. The feature computing resource cost represents the cost of using the target feature in the target model in terms of computing resources such as CPU, memory, network resources, etc. A tradeoff between feature importance and feature computing resource cost can be made to decide whether to have the target machine learning model use or not use the target machine learning feature in production, thereby improving the production multistage feed item ranking system and solving the technical problem of determining which machine learning features of a machine learning model represent the best tradeoff between feature importance and feature computing resource cost.

Abstract: A feature importance score for a target machine learning feature of a target machine learning model used in a multistage feed ranking system for scoring feed items is supplemented with a feature computing resource cost. The feature computing resource cost represents the cost of using the target feature in the target model in terms of computing resources such as CPU, memory, network resources, etc. A tradeoff between feature importance and feature computing resource cost can be made to decide whether to have the target machine learning model use or not use the target machine learning feature in production, thereby improving the production multistage feed item ranking system and solving the technical problem of determining which machine learning features of a machine learning model represent the best tradeoff between feature importance and feature computing resource cost.

Abstract: Computer-implemented techniques for approximating recall of a first pass ranker at the second pass ranking stage in a scalable manner. The techniques are efficient in that they do not require the second pass ranker to score all feed items considered by the first pass ranker in order to approximate the recall. Instead, the recall is approximated with a fewer number of feed item for which scores are already logged. Because the first pass ranker scores and the second pass ranker scores are already logged and available at a time of recall approximation, the techniques are computationally efficient. At the same time, using the scores of the fewer number of feed items still gives a good approximation of the recall at the second pass ranking stage.

Abstract: Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model—that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system—with the more lightweight, single objective model—that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.