Search Results for author:
Found 19 papers, 4 papers with code
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible.
HiGPT: Heterogeneous Graph Language Model
However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets.
UrbanGPT: Spatio-Temporal Large Language Models
These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce.
Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks
Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content.
Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis
In the SAP, the semantic knowledge learned from the source lesion domain is transferred to consecutive target lesion domains.
Sequential Recommendation with User Evolving Preference Decomposition
To solve this problem, in this paper, we propose a novel sequential recommender model via decomposing and modeling user independent preferences.
User behavior understanding in real world settings
It is necessary to learn a dynamic group of representations according the item groups in a user historical behavior.
Gumble Softmax For User Behavior Modeling
We propose a sequential model with dynamic number of representations for recommendation systems (RDRSR).
Data-Efficient Reinforcement Learning for Malaria Control
This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials.
Neural Interactive Collaborative Filtering
Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning.
Off-policy Learning for Multiple Loggers
To make full use of such historical data, learning policies from multiple loggers becomes necessary.
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
Though reinforcement learning~(RL) naturally fits the problem of maximizing the long term rewards, applying RL to optimize long-term user engagement is still facing challenges: user behaviors are versatile and difficult to model, which typically consists of both instant feedback~(e. g. clicks, ordering) and delayed feedback~(e. g. dwell time, revisit); in addition, performing effective off-policy learning is still immature, especially when combining bootstrapping and function approximation.
Whole-Chain Recommendations
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.
Multi-agent Reinforcement Learning
Recommendation Systems
+1
Deep reinforcement learning for search, recommendation, and online advertising: a survey
Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web.
Deep Reinforcement Learning for Page-wise Recommendations
In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations.
Deep Reinforcement Learning for List-wise Recommendations
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services.
A Surrogate-based Generic Classifier for Chinese TV Series Reviews
With the emerging of various online video platforms like Youtube, Youku and LeTV, online TV series' reviews become more and more important both for viewers and producers.
