Search Results for author:
Found 11 papers, 2 papers with code
AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
We propose AutoCrawler, a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding.
Character is Destiny: Can Large Language Models Simulate Persona-Driven Decisions in Role-Playing?
Can Large Language Models substitute humans in making important decisions?
Mixed Distillation Helps Smaller Language Model Better Reasoning
While large language models (LLMs) have demonstrated exceptional performance in recent natural language processing (NLP) tasks, their deployment poses substantial challenges due to high computational and memory demands in real-world applications.
Modeling User Viewing Flow Using Large Language Models for Article Recommendation
This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles.
Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing
Demand estimation plays an important role in dynamic pricing where the optimal price can be obtained via maximizing the revenue based on the demand curve.
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms.
SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios
In this paper, we propose a novel Scenario-Aware Ranking Network (SAR-Net) to address these issues.
Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning
Therefore, in this paper, we focus on predicting users' intention destinations in online travel platforms.
LHRM: A LBS based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform
To alleviate this problem, existing user cold start methods either apply deep learning to build a cross-domain recommender system or map user attributes into the space of user behaviour.
Itinerary-aware Personalized Deep Matching at Fliggy
All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred.
Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
Motivated by this observation, we propose a novel \emph{CVR} prediction method by Hierarchically Modeling both Micro and Macro behaviors ($HM^3$).
