Interactive Search Based on Deep Reinforcement Learning
- URL: http://arxiv.org/abs/2012.06052v1
- Date: Wed, 9 Dec 2020 15:23:53 GMT
- Title: Interactive Search Based on Deep Reinforcement Learning
- Authors: Yang Yu, Zhenhao Gu, Rong Tao, Jingtian Ge, Kenglun Chang
- Abstract summary: The project mainly establishes a virtual user environment for offline training.
At the same time, we tried to improve a reinforcement learning algorithm based on bi-clustering to expand the action space and recommended path space of the recommendation agent.
- Score: 4.353144350714567
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: With the continuous development of machine learning technology, major
e-commerce platforms have launched recommendation systems based on it to serve
a large number of customers with different needs more efficiently. Compared
with traditional supervised learning, reinforcement learning can better capture
the user's state transition in the decision-making process, and consider a
series of user actions, not just the static characteristics of the user at a
certain moment. In theory, it will have a long-term perspective, producing a
more effective recommendation. The special requirements of reinforcement
learning for data make it need to rely on an offline virtual system for
training. Our project mainly establishes a virtual user environment for offline
training. At the same time, we tried to improve a reinforcement learning
algorithm based on bi-clustering to expand the action space and recommended
path space of the recommendation agent.
Related papers
- On the Opportunities and Challenges of Offline Reinforcement Learning
for Recommender Systems [36.608400817940236]
Reinforcement learning serves as potent tool for modeling dynamic user interests within recommender systems.
Recent strides in offline reinforcement learning present a new perspective.
Despite being a burgeoning field, works centered on recommender systems utilizing offline reinforcement learning remain limited.
arXiv Detail & Related papers (2023-08-22T10:28:02Z) - Scalable and Robust Self-Learning for Skill Routing in Large-Scale
Conversational AI Systems [13.705147776518421]
State-of-the-art systems use a model-based approach to enable natural conversations.
We propose a scalable self-learning approach to explore routing alternatives.
arXiv Detail & Related papers (2022-04-14T17:46:14Z) - ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement
Learning [91.58711082348293]
Reinforcement learning from online user feedback on the system's performance presents a natural solution to this problem.
This approach tends to require a large amount of human-in-the-loop training data, especially when feedback is sparse.
We propose a hierarchical solution that learns efficiently from sparse user feedback.
arXiv Detail & Related papers (2022-02-05T02:01:19Z) - Incremental Learning for Personalized Recommender Systems [8.020546404087922]
We present an incremental learning solution to provide both the training efficiency and the model quality.
The solution is deployed in LinkedIn and directly applicable to industrial scale recommender systems.
arXiv Detail & Related papers (2021-08-13T04:21:21Z) - Offline Preference-Based Apprenticeship Learning [11.21888613165599]
We study how an offline dataset can be used to address two challenges that autonomous systems face when they endeavor to learn from, adapt to, and collaborate with humans.
First, we use the offline dataset to efficiently infer the human's reward function via pool-based active preference learning.
Second, given this learned reward function, we perform offline reinforcement learning to optimize a policy based on the inferred human intent.
arXiv Detail & Related papers (2021-07-20T04:15:52Z) - Generative Adversarial Reward Learning for Generalized Behavior Tendency
Inference [71.11416263370823]
We propose a generative inverse reinforcement learning for user behavioral preference modelling.
Our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN.
arXiv Detail & Related papers (2021-05-03T13:14:25Z) - Generative Inverse Deep Reinforcement Learning for Online Recommendation [62.09946317831129]
We propose a novel inverse reinforcement learning approach, namely InvRec, for online recommendation.
InvRec extracts the reward function from user's behaviors automatically, for online recommendation.
arXiv Detail & Related papers (2020-11-04T12:12:25Z) - Rethinking Supervised Learning and Reinforcement Learning in
Task-Oriented Dialogue Systems [58.724629408229205]
We demonstrate how traditional supervised learning and a simulator-free adversarial learning method can be used to achieve performance comparable to state-of-the-art RL-based methods.
Our main goal is not to beat reinforcement learning with supervised learning, but to demonstrate the value of rethinking the role of reinforcement learning and supervised learning in optimizing task-oriented dialogue systems.
arXiv Detail & Related papers (2020-09-21T12:04:18Z) - Empowering Active Learning to Jointly Optimize System and User Demands [70.66168547821019]
We propose a new active learning approach that jointly optimize the active learning system (training efficiently) and the user (receiving useful instances)
We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user.
We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
arXiv Detail & Related papers (2020-05-09T16:02:52Z) - Knowledge-guided Deep Reinforcement Learning for Interactive
Recommendation [49.32287384774351]
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy.
We propose Knowledge-Guided deep Reinforcement learning to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation.
arXiv Detail & Related papers (2020-04-17T05:26:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.