Search Results for author:
Found 13 papers, 4 papers with code
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
Multi-channel Integrated Recommendation with Exposure Constraints
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
Entire Space Learning Framework: Unbias Conversion Rate Prediction in Full Stages of Recommender System
Although existing methods ESMM and ESM2 train with all impression samples over the entire space by modeling user behavior paths, SSB and DS problems still exist.
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.
Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking
This paper proposes a Hierarchical Multi-Interest Co-Network (HCN) to capture users' diverse interests in the coarse-grained ranking stage.
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.
Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction
The results verify that the proposed model outperforms existing CTR models considerably, in terms of both CTR prediction performance and online cost-efficiency.
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.
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.
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks
Experiments on English data in the CoNLL 2009 shared task show that our method largely reduced the performance drop on out-of-domain test data.
