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Found 20 papers, 4 papers with code
Domain-Aware Cross-Attention for Cross-domain Recommendation
Cross-domain recommendation (CDR) is an important method to improve recommender system performance, especially when observations in target domains are sparse.
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
To address the aforementioned issue, we propose an incremental update framework for online recommenders with Data-Driven Prior (DDP), which is composed of Feature Prior (FP) and Model Prior (MP).
Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style
Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style directly from a reference image to resolve textual ambiguities, and is trained in a self-supervised manner for more efficient training data usage.
Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems
In this paper, we propose for the first time a novel architecture for online parallel estimation of ads and creatives ranking, as well as the corresponding offline joint optimization model.
Data Contamination Issues in Brain-to-Text Decoding
Decoding non-invasive cognitive signals to natural language has long been the goal of building practical brain-computer interfaces (BCIs).
Planning and Rendering: Towards End-to-End Product Poster Generation
At the planning stage, we propose a PlanNet to generate the layout of the product and other visual components considering both the appearance features of the product and semantic features of the text, which improves the diversity and rationality of the layouts.
Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking.
Confidence Ranking for CTR Prediction
Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e. g. ads and recommendation systems.
Mutual Query Network for Multi-Modal Product Image Segmentation
Product image segmentation is vital in e-commerce.
Relation-Aware Diffusion Model for Controllable Poster Layout Generation
To advance research in this field, we have constructed a poster layout dataset named CGL-Dataset V2.
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Streaming data has the characteristic that the underlying distribution drifts over time and may recur.
CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection
Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion.
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving
In this work, we propose a novel framework PCDF(Parallel-Computing Distributed Framework), allowing to split the computation cost into three parts and to deploy them in the pre-module in parallel with the retrieval stage, the middle-module for ranking ads, and the post-module for re-ranking ads with external items.
NDGGNET-A Node Independent Gate based Graph Neural Networks
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on.
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction
The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction.
IA-GCN: Interactive Graph Convolutional Network for Recommendation
In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN).
On the Adaptation to Concept Drift for CTR Prediction
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying.
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction
Click-through rate (CTR) Prediction is of great importance in real-world online ads systems.
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction.
Dynamic Parameterized Network for CTR Prediction
Learning to capture feature relations effectively and efficiently is essential in click-through rate (CTR) prediction of modern recommendation systems.
