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Found 20 papers, 4 papers with code
Oblique-MERF: Revisiting and Improving MERF for Oblique Photography
Neural implicit fields have established a new paradigm for scene representation, with subsequent work achieving high-quality real-time rendering.
City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
Existing neural radiance field-based methods can achieve real-time rendering of small scenes on the web platform.
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
Later the augmentation network employs the explicit cross-domain knowledge as augmented information to boost the target domain CTR prediction.
Large Language Model based Long-tail Query Rewriting in Taobao Search
In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue.
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search
Graph-based CF methods improve personalization by modeling collaborative signal within the user click graph.
COURIER: Contrastive User Intention Reconstruction for Large-Scale Pre-Train of Image Features
To this end, we propose a recommendation-aware image pre-training method that can learn visual features from user click histories.
Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System
As such, the metric of a pre-ranking model follows the ranking model using Area Under ROC (AUC) for offline evaluation.
Image to Multi-Modal Retrieval for Industrial Scenarios
We formally define a novel valuable information retrieval task: image-to-multi-modal-retrieval (IMMR), where the query is an image and the doc is an entity with both image and textual description.
Cross-domain Augmentation Networks for Click-Through Rate Prediction
The translation network is able to compute features from two domains with heterogeneous inputs separately by designing two independent branches, and then learn meaningful cross-domain knowledge using a designed cross-supervised feature translator.
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training
E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries.
MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search
Given a user query, the retrieval phase returns a subset of candidate products for the following ranking phase.
Multi-Objective Personalized Product Retrieval in Taobao Search
These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval.
Intelligent Request Strategy Design in Recommender System
RSs typically put a large number of items into one page to reduce excessive resource consumption from numerous paging requests, which, however, would diminish the RSs' ability to timely renew the recommendations according to users' real-time interest and lead to a poor user experience.
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
In this paper, we propose a new perspective for context-aware users' behavior modeling by including the whole page-wisely exposed products and the corresponding feedback as contextualized page-wise feedback sequence.
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
On this basis, we develop a specific interactive hypergraph neural network to explicitly encode the structure information (i. e., collaborative signal) into the embedding process.
Embedding-based Product Retrieval in Taobao Search
We evaluate MGDSPR on Taobao Product Search with significant metrics gains observed in offline experiments and online A/B tests.
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution.
Neuron-level Structured Pruning using Polarization Regularizer
Experimentally, we show that structured pruning using polarization regularizer achieves much better results than using L1 regularizer.
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations.
A Minimax Game for Instance based Selective Transfer Learning
To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning.
