推荐系统

The main contribution of this thesis is that we propose a collaborative filtering recommendation algorithm based on double clustering. This approach first respectively clusters resources and users by the users rating on items, then makes a collaborative filtering recommendation based on the clustering result. The new algorithm can shorten on-line recommendation time. Then, we apply the classical formula of cosine correlation to double clustering algorithm, leaving out the standardization operation. In the end, we implement an educational resource recommender system according to the need of the actual project and the result is positive.

本文的主要意义在于，首先运用聚类技术对用户和资源分别进行聚类，然后利用聚类结果进行协同过滤推荐，由于聚类部分离线周期进行，大大缩短了在线的推荐时间；然后将经典的余弦相似性计算公式运用到双重聚类算法中，省去规范化处理操作，减少运算量；最后结合实际的需要，实现一个资源推荐系统。

The results showed that the new podcast recommendation system is well capable of recommending the pertinency programs in line with users' preference.

试验结果证明，该播客推荐系统能很好地根据用户的喜好特点来进行针对性节目推荐。

And then expands it to an open framework for recommendation system. Based on PIR framework, we can integrate new algorithms easily.

之后阐述了智能推荐系统产生的必要性，详细讨论了智能推荐系统在电子商务和数字图书馆系统当中的应用，总结了当前比较有代表性的各种智能推荐算法，并给出了对智能推荐算法的有效评价方法。

All of them, collaborative filtering is regarded as most popular and successful personalized recommendatory algorithm in personalized recommendation system.

其中协同过滤被认为是至今为止在信息资源个性化推荐系统中最常用、最成功的一种个性化推荐技术。

Perhaps the biggest issue facing recommender systems is that they need a lot of data to effectively make recommendations.

推荐系统的最大问题是它们需要很大的数据量来做出好的推荐。

This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field.

全面地总结推荐系统的研究现状，旨在介绍网络推荐的算法思想、帮助读者了解这个研究领域。

However, the recommender system still faces a series of challenges. Thereby, research on recommendation system is of most realistic significance.

然而，推荐系统也面临着一系列的挑战，所以对推荐系统的研究具有非常重要的现实意义。

In this thesis, we try to apply Recommender System in web sites to help users finding their favorite web sites, and to solve the problems of traditional model.

本研究尝试将推荐系统应用在网站上，欲藉由此机制帮助使用者寻找喜爱的网站，同时尽力解决一般推荐系统所遇到的问题。

To overcome the difficulty of data sparsity in recommendation systems, a collaborative filtering algorithm based on clustering basal users is presented.

为了降低数据稀疏性的影响，提高推荐系统的推荐生成质量，提出了一种基于多层相似性用户聚类的协同过滤推荐算法。

In this paper a new information recommendation system-Carlnfo is introduced. It takes the relations among words into account. It can make scattered keywords into a meshy structure so as to provide an abstract view of users" characters. It can distinguish explicit interests with latent interests. The modification of user profile is made based on user"s actual choose. Structuring of ontology is realized semi-automatically.

本文设计了一个信息推荐系统CarInfo，在这个系统中引入了人工智能界的一个概念Ontology，它根据词之间的关系把零散的关键词组织成一个网状结构，提供了一个用户特征的抽象视图，在推荐时系统能够区分用户的明确兴趣和潜在兴趣，并根据用户的实际选择对用户模型进行修正。

Singer Leona Lewis and former Led Zeppelin guitarist Jimmy Page emerged as the bus transformed into a grass-covered carnival float, and the pair combined for a rendition of "Whole Lotta Love".

歌手leona刘易斯和前率领的飞艇的吉他手吉米页出现巴士转化为基层所涵盖的嘉年华花车，和一双合并为一移交&整个lotta爱&。

This is Kate, and that's Erin.

这是凯特，那个是爱朗。