Search Results for author:
Found 5 papers, 1 papers with code
Reinforcement Re-ranking with 2D Grid-based Recommendation Panels
Presenting items in grid-based result panels poses new challenges to recommender systems because existing models are all designed to output sequential lists while the slots in a grid-based panel have no explicit order.
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.
