Search Results for author:
Found 14 papers, 8 papers with code
LLM-Alignment Live-Streaming Recommendation
In recent years, integrated short-video and live-streaming platforms have gained massive global adoption, offering dynamic content creation and consumption.
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation
However, the live-streaming services faces serious data-sparsity problem, which can be attributed to the following two points: (1) User's valuable behaviors are usually sparse, e. g., like, comment and gift, which are easily overlooked by the model, making it difficult to describe user's personalized preference.
ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling
Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e. g., e-commerce and advertising.
Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach
Multifaceted user modeling aims to uncover fine-grained patterns and learn representations from user data, revealing their diverse interests and characteristics, such as profile, preference, and personality.
QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou
In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling.
A Unified Framework for Cross-Domain Recommendation
In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising methodology.
Moment&Cross: Next-Generation Real-Time Cross-Domain CTR Prediction for Live-Streaming Recommendation at Kuaishou
Kuaishou, is one of the largest short-video and live-streaming platform, compared with short-video recommendations, live-streaming recommendation is more complex because of: (1) temporarily-alive to distribution, (2) user may watch for a long time with feedback delay, (3) content is unpredictable and changes over time.
Federated Adaptation for Foundation Model-based Recommendations
It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy.
End-to-end training of Multimodal Model and ranking Model
In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption.
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
For existing old ads, GMEs first build a graph to connect them with new ads, and then adaptively distill useful information.
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad.
Click-Through Rate Prediction with the User Memory Network
Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services.
Representation Learning-Assisted Click-Through Rate Prediction
These subnets model the user-ad, ad-ad and feature-CTR relationship respectively.
Ranked #2 on
Click-Through Rate Prediction
on Avito
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent.
Ranked #1 on
Click-Through Rate Prediction
on Avito
