Search Results for author:
Found 18 papers, 4 papers with code
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model.
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce
An effective ranking model should give a personalized ranking list for each user according to the user preference.
Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation
In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL).
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products.
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval
However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories.
Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search
Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing.
Face Clustering via Graph Convolutional Networks with Confidence Edges
Experiments show that our method outperforms existing methods on the face and person datasets to achieve state-of-the-art.
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
BERT-style models pre-trained on the general corpus (e. g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on.
Sequential Search with Off-Policy Reinforcement Learning
Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.
An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration
Conversion rate (CVR) prediction is becoming in- creasingly important in the multi-billion dollar on- line display advertising industry.
SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search
Graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully applied to industrial-scale search engine yet.
Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index
Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems.
Query Rewriting via Cycle-Consistent Translation for E-Commerce Search
Online A/B experiments show that it improves core e-commerce business metrics significantly.
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search
We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
Relevance has significant impact on user experience and business profit for e-commerce search platform.
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.
Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
In addition, feature importance for the purpose of CTR/CVR predictions differs from one category to another.
Category-Specific CNN for Visual-aware CTR Prediction at JD.com
Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.
