Search Results for author:
Found 12 papers, 4 papers with code
Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Besides, by personalized integration of domain features from other domains for each user and the innovation in the training mode, the DFEI framework can yield more accurate conversion identification.
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
Thus, in this paper, we further propose a transfer framework to tackle the cross-domain fraud detection problem, which aims to transfer knowledge from existing domains (source domains) with enough and mature data to improve the performance in the new domain (target domain).
Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification
To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users.
Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance.
Domain Adaptation with Category Attention Network for Deep Sentiment Analysis
Domain adaptation tasks such as cross-domain sentiment classification aim to utilize existing labeled data in the source domain and unlabeled or few labeled data in the target domain to improve the performance in the target domain via reducing the shift between the data distributions.
Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification
Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users' dynamic preferences.
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage.
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.
Graph Factorization Machines for Cross-Domain Recommendation
In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation.
Transfer Learning Toolkit: Primers and Benchmarks
The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function.
A Comprehensive Survey on Transfer Learning
In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.
