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Found 7 papers, 2 papers with code
Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce
It means the modification is tailored to and only to the current request to capture the specific context of the request.
Controllable Multi-Objective Re-ranking with Policy Hypernetworks
In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights.
Reinforcement Re-ranking with 2D Grid-based Recommendation Panels
Then, it defines \emph{the MDP discrete time steps as the ranks in the initial ranking list, and the actions as the prediction of the user-item preference and the selection of the slots}.
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
In addition, our methods are based on the multi-task learning framework and mitigate the data sparsity issue.
Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
Although existing methods, typically built on the user sequential behavior path ``impression$\to$click$\to$purchase'', is effective for dealing with SSB issue, they still struggle to address the DS issue due to rare purchase training samples.
SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors.
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System
The deep cascade structure and the combination rule enable the proposed \textit{ldcTree} to have a stronger distributed feature representation ability.
