COLLABORATIVE FILTERING RECOMMENDATIONALGORITHM BASED ON LOOK-AHEAD SELECTIVESAMPLING

机译：基于前瞻选择性采样的协同过滤推荐算法

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Personalized Recommendation System has become anrnimportant research item to prove the suitable product andrnservices for individual. And classification of customersrnbecomes the basis to produce recommendation. In a realisticrnEC system, the magnitudes of customers and products are allrnhuge, so the quality of recommendation decreasesrndramatically. To improve recommending quantity, arncollaborative filtering model was proposed based on lookaheadrnsampling. In n-dimension Euclid space constituted byrnusers, the proposed algorithm reduces the number of samplesrnwhile maintaining the quality of classification, throughrnestimating sample's utility for classifier. At last, experimentsrnwere designed at the basis of MoveLens dataset. Comparedrnwith general collaborative filtering, the proposed algorithmrnhas higher quality of recommendation.

机译：个性化推荐系统已成为一项重要的研究项目，以证明适合个人的产品和服务。并将客户分类成为产生推荐的依据。在一个现实的EC系统中，客户和产品的规模很大，因此推荐的质量急剧下降。为了提高推荐量，提出了基于前瞻性抽样的arncollaborative过滤模型。在由用户组成的n维Euclid空间中，该算法通过估计样本在分类器中的效用，在保持分类质量的同时减少了样本数。最后，在MoveLens数据集的基础上进行了实验设计。与一般协同过滤相比，该算法具有较高的推荐质量。

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来源
《中国机械工程学会年会暨中国工程院机械与运载工程学部首届年会》|2006年|1-5|共5页
会议地点 Hangzhou(CN)
作者
GAO Linqi; LI Congdong;
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作者单位

Management School of Tianjin University, Tianjin 300073, China Management School of Tianjin Normal University, Tianjin 300387, China Email: gaolqi@mail.tjnu.edu.cn;

rnManagement School of Tianjin University, Tianjin 300073, China;

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原文格式 PDF
正文语种 eng
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关键词
Nearest neighbour algorithm; look-ahead algorithm; selective sampling; recommendation system;

机译：最近邻居算法；前瞻算法选择性抽样；推荐系统;

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专利

1. CF4J 2.0: Adapting Collaborative Filtering for Java to new challenges of collaborative filtering based recommender systems [J] . Ortega F., Mayor J., Lopez-Fernandez D., Knowledge-Based Systems . 2021,第Mara5期

机译：CF4J 2.0：适应基于协同过滤的推荐系统的新挑战，适应Java的新挑战
2. Look-ahead Proposals For Robust Grid-based Slam With Rao-blackwellized Particle Filters [J] . Slawomir Grzonka, Christian Plagemann, Giorgio Grisetti, The International journal of robotics research . 2009,第2期

机译：具有Rao黑井粒子过滤器的稳健基于网格的Slam的前瞻性建议
3. Annihilation-reordering look-ahead pipelined CORDIC-based RLS adaptive filters and their application to adaptive beamforming [J] . Jun Ma, Parhi K.K. IEEE Transactions on Signal Processing . 2000,第8期

机译：灭-重排序超前流水线基于CORDIC的RLS自适应滤波器及其在自适应波束形成中的应用
4. COLLABORATIVE FILTERING RECOMMENDATION ALGORITHM BASED ON LOOK-AHEAD SELECTIVE SAMPLING [C] . . 2006

机译：基于前瞻选择性采样的协同过滤推荐算法
5. A learning-based model in clickstream-based collaborative filtering. [D] . Kim, Dong-Ho. 2005

机译：基于点击流的协作过滤中的基于学习的模型。
6. Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering [O] . Tae-Yeun Kim, Hoon Ko, Sung-Hwan Kim, 2021

机译：基于情感信息和协作筛选的推荐系统建模
7. Look-ahead Proposals for Robust Grid-based SLAM with Rao-Blackwellized Particle Filters [O] . Slawomir Grzonka, Giorgio Grisetti, Christian Plagemann, 2012

机译：基于Rao-Blackwellized粒子滤波器的基于网格的鲁棒sLam的前瞻性建议

COLLABORATIVE FILTERING RECOMMENDATIONALGORITHM BASED ON LOOK-AHEAD SELECTIVESAMPLING

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