RLCF: A collaborative filtering approach based on reinforcement learning with sequential ratings

Lee Jungkyu; Oh Byonghwa; Yang Jihoon; Park Unsang

首页> 外文期刊>Intelligent automation and soft computing >RLCF: A collaborative filtering approach based on reinforcement learning with sequential ratings

【24h】

RLCF: A collaborative filtering approach based on reinforcement learning with sequential ratings

机译：RLCF：基于强化学习和顺序评分的协作过滤方法

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

We present a novel approach for collaborative filtering, RLCF, that considers the dynamics of user ratings. RLCF is based on reinforcement learning applied to the sequence of ratings. First, we formalize the collaborative filtering problem as a Markov Decision Process. Then, we learn the connection between the temporal sequences of user ratings using Q-learning. Experiments demonstrate the feasibility of our approach and a tight relationship between the past and the current ratings. We also suggest an ensemble learning in RLCF and demonstrate its improved performance.

机译：我们提出了一种用于协作过滤的新颖方法RLCF，该方法考虑了用户评分的动态变化。 RLCF基于应用于等级序列的强化学习。首先，我们将协作过滤问题形式化为马尔可夫决策过程。然后，我们使用Q学习学习用户评分的时间序列之间的联系。实验证明了我们方法的可行性以及过去与当前评级之间的紧密关系。我们还建议在RLCF中进行整体学习，并演示其改进的性能。

著录项

来源
《Intelligent automation and soft computing》 |2017年第3期|439-444|共6页
作者
Lee Jungkyu; Oh Byonghwa; Yang Jihoon; Park Unsang;
展开▼
作者单位

Daum Kakao Corp, Search Qual Team, Seoul, South Korea;

Dept Comp Sci & Engn, Seoul, South Korea;

Dept Comp Sci & Engn, Seoul, South Korea;

Sogang Univ, Dept Comp Sci & Engn, Seoul, South Korea;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词
Recommender systems; Markov decision process; Q-learning; Ensemble learning;

机译：推荐系统;马尔可夫决策过程;Q学习;集成学习;

相似文献

外文文献
中文文献
专利

1. A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis [J] . Keunho Choi, Donghee Yoo, Gunwoo Kim, Electronic commerce research and applications . 2012,第1a6期

机译：混合在线产品推荐系统：结合基于隐式评分的协作过滤和顺序模式分析
2. Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering [J] . Mojtaba Salehi, Isa Nakhai Kamalabadi, Mohammad Bagher Ghaznavi Ghoushchi Education and information technologies . 2014,第4期

机译：使用顺序模式挖掘和基于属性的协作过滤对学习材料进行个性化推荐
3. A COLLABORATIVE FILTERING BASED APPROACH TO CLASSIFY MOVIE GENRES USING USER RATINGS [J] . RAJI GHAWI, JUERGEN PFEFFER Journal of Data Intelligence . 2020,第4期

机译：基于协作的过滤方法来使用用户评级对电影流派进行分类
4. Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners' rating [C] . Rudolf Turnip, Dade Nurjanah, Dana Sulistyo Kusumo IEEE Conference on e-Learning, e-Management and e-Services . 2017

机译：混合推荐系统，用于使用基于内容的过滤和具有良好学习者评级的协作过滤来学习材料
5. Enhancing Collaborative Filtering-Based Rating-Prediction by Discovering and Incorporating User Concerns from User Reviews [D] . Pradhan, Ligaj. 2017

机译：通过发现和纳入用户评论中的用户关注点来增强基于协作过滤的评分预测
6. Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems [O] . Mubbashir Ayub, Mustansar Ali Ghazanfar, Zahid Mehmood, 2012

机译：对用户评级偏好行为进行建模，以提高基于协作过滤的推荐系统的性能
7. Conquering the rating bound problem in neighborhood-based collaborative filtering: a function recovery approach [O] . Huang, Junming, Cheng, Xue-Qi, Shen, Hua-Wei, 2012

机译：克服基于邻域的协作中的评级约束问题过滤：功能恢复方法

RLCF: A collaborative filtering approach based on reinforcement learning with sequential ratings

摘要

著录项

相似文献

相关主题

期刊订阅