Constrained Multi-Slot Optimization for Ranking Recommendations
This addresses the problem of multi-slot ranking for social media applications, offering an incremental improvement by incorporating interaction effects that previous single-slot methods ignored.
The paper tackles the problem of ranking recommendations by modeling interactions among items across multiple slots, formulating it as a quadratically constrained quadratic program (QCQP) and providing an efficient approximate solver. Through simulated experiments, it demonstrates benefits in modeling interactions and shows improved speed and accuracy compared to state-of-the-art methods.
Ranking items to be recommended to users is one of the main problems in large scale social media applications. This problem can be set up as a multi-objective optimization problem to allow for trading off multiple, potentially conflicting objectives (that are driven by those items) against each other. Most previous approaches to this problem optimize for a single slot without considering the interaction effect of these items on one another. In this paper, we develop a constrained multi-slot optimization formulation, which allows for modeling interactions among the items on the different slots. We characterize the solution in terms of problem parameters and identify conditions under which an efficient solution is possible. The problem formulation results in a quadratically constrained quadratic program (QCQP). We provide an algorithm that gives us an efficient solution by relaxing the constraints of the QCQP minimally. Through simulated experiments, we show the benefits of modeling interactions in a multi-slot ranking context, and the speed and accuracy of our QCQP approximate solver against other state of the art methods.