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   协同过滤技术 的翻译结果: 查询用时:0.008秒
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协同过滤技术
相关语句
  technology of collaborative filtering
     Study on the Technology of Collaborative Filtering Baseed on the Order of Time in the Personalized Information Service
     基于时间序列的个性化信息协同过滤技术研究
短句来源
     At present,the recommendation system mainly use the technology of collaborative filtering(CF).
     目前使用的推荐系统绝大部分是基于协同过滤技术的推荐,协同过滤是一种基于客户交易偏好的对所有客户的无差别推荐,这可能会导致企业在获利甚微的客户身上投入不适当的成本。
短句来源
  “协同过滤技术”译为未确定词的双语例句
     On this basis, the thesis takes into account the characteristics of Chinese information processing and makes use of multi-technology to come up with a personalized Chinese information service system for digital libraries to fulfill users' needs.
     基于这种背景,论文在分析已有个性化信息服务系统的基础上,引入用户建模技术、Web数据挖掘技术和协同过滤技术,针对中文文本自身的特点及由这些特点带来的信息处理的特殊性,构建了一个数字图书馆个性化中文文本信息服务系统,力图在用户的最小努力下,为其提供更多、更贴近其需求的信息。
短句来源
     CRM Active Marketing Model Based on Collaborative Filtering
     基于协同过滤技术的CRM主动营销模型研究
短句来源
     The integration filtering can be used in reference in other individualized information services such as E-business.
     (2)提出一种兼具个体过滤技术和协同过滤技术优点的综合过滤技术,对其它基于网络的个性化信息服务如电子商务有借鉴意义;
短句来源
     a Survey of E-Commerce Recommendation System Based on Collaborative Filtering
     基于协同过滤技术的电子商务推荐系统初探
短句来源
     A Study on Item-Based Collaborative Filtering Algorithm Using Semantic Similarity
     基于语义相似性的资源协同过滤技术研究
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  相似匹配句对
     Collaborative Filtering
     信息协同过滤
短句来源
     Filtration Technology and Facilities
     过滤技术及装置
短句来源
     Co-design
     协同设计技术
短句来源
     Air nitration techenologies for dust collection
     过滤除尘技术
短句来源
     a Survey of E-Commerce Recommendation System Based on Collaborative Filtering
     基于协同过滤技术的电子商务推荐系统初探
短句来源
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Collaborative filtering is the most successful technology for building recommendation systems. Unfortunately,the efficiency of these methods decline linearly with the number of users and items .To address these limitations, a high efficient personalization recommendation algorithm is presented, which includes two phases: dimensionality reduction and item-based recommendation methods. This algorithm reduces the computation consumption based on enhancing the accuracy, etc. It may solve questions well such as sparsity,...

Collaborative filtering is the most successful technology for building recommendation systems. Unfortunately,the efficiency of these methods decline linearly with the number of users and items .To address these limitations, a high efficient personalization recommendation algorithm is presented, which includes two phases: dimensionality reduction and item-based recommendation methods. This algorithm reduces the computation consumption based on enhancing the accuracy, etc. It may solve questions well such as sparsity, scalability. It can create accurate personalization recommendation quickly.

协同过滤技术 (collaborative filtering)目前被成功地应用于个性化推荐系统中 ,但随着系统规模的扩大 ,它的效能逐渐降低 ,针对它的缺点 ,提出了一种高效的个性化推荐算法 ,它包括维数简化和项集相似性计算两个过程 ,这种算法在提高精确性的基础上减少了计算耗费 ,可以较好地解决应用协同过滤技术的推荐系统所存在的稀疏性、扩展性等问题 ,快速产生精确的个性化推荐结果

The essentiality of active marketing in the CRM is presented,then an active marketing model based on collaborative filtering is proposed,and the similarity computation methods of this model are stated in detail.Its feasibility is proved by an experiment,and the capabilities of the similarity computation methods are showed in the MAE point.

叙述在 CRM中进行主动营销的重要性 ,提出基于协同过滤技术的 CRM主动营销模型 ,对模型中常用的项目相似度计算方法进行详细阐述 ,然后通过实验证明模型的可行性 ,并从平均绝对偏差角度说明几种相似度计算方法的性能。

Collaborative filtering is used extensively in personalized recommendation systems With the development of E commerce, the magnitudes of users and commodities grow rapidly, resulting in the extreme sparsity of user rating data Traditional similarity measure methods work poor in this situation Considering the extreme sparsity of user rating data, collaborative filtering algorithm based on item rating prediction is introduced, then the item similarity is computed by using a new revised conditional probability...

Collaborative filtering is used extensively in personalized recommendation systems With the development of E commerce, the magnitudes of users and commodities grow rapidly, resulting in the extreme sparsity of user rating data Traditional similarity measure methods work poor in this situation Considering the extreme sparsity of user rating data, collaborative filtering algorithm based on item rating prediction is introduced, then the item similarity is computed by using a new revised conditional probability expression, the quality of the recommended result can be effectively improved Finally an optimized collaborative filtering recommendation algorithm is presented It can be proved that the new algorithm presented has a better performance corresponding to the known algorithm The experiment shows that the approach is successful

协同过滤技术被成功地应用于个性化推荐系统中 随着电子商务系统用户数目和商品数目的日益增加 ,整个项目空间上用户评分数据极端稀疏 ,传统的相似性度量方法存在一定的不足 在引入项目评分预测思想的基础上 ,考虑到数据稀疏性带来的影响 ,采用修正的条件概率方法计算项目相似性 ,提出一种优化的协同过滤推荐算法 ,计算结果更具有实际意义和准确性 实验表明 ,该算法能够有效避免传统方法带来的弊端 ,提高系统的推荐质量

 
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