research
∙
06/29/2023
Multi-Scenario Ranking with Adaptive Feature Learning
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation...
share
research
∙
09/04/2022
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
Deep learning techniques have been applied widely in industrial recommen...
research
∙
08/22/2022
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
An industrial recommender system generally presents a hybrid list that c...
research
∙
08/12/2022
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
Despite the development of ranking optimization techniques, the pointwis...
research
∙
02/14/2022
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
Model-based methods for recommender systems have been studied extensivel...
research
∙
12/21/2021
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
Nowadays, data-driven deep neural models have already shown remarkable p...
research
∙
11/01/2021
Comparative Explanations of Recommendations
As recommendation is essentially a comparative (or ranking) process, a g...
research
∙
09/26/2021
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction
In this paper, we propose a novel model named DemiNet (short for DEpende...
research
∙
05/18/2021
Path-based Deep Network for Candidate Item Matching in Recommenders
The large-scale recommender system mainly consists of two stages: matchi...
research
∙
02/14/2021
Learning a Product Relevance Model from Click-Through Data in E-Commerce
The search engine plays a fundamental role in online e-commerce systems,...
research
∙
01/24/2021
Explanation as a Defense of Recommendation
Textual explanations have proved to help improve user satisfaction on ma...
research
∙
05/21/2020
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
In a large recommender system, the products (or items) could be in many ...
research
∙
05/21/2020
ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
Most of ranking models are trained only with displayed items (most are h...
research
∙
01/13/2020
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Large pre-trained language models such as BERT have shown their effectiv...
research
∙
05/26/2019
