Result shows that the proved algorithm can be more effective and accurate than traditional collaborative filtering algorithms for new user by contrastive experiment.

为了提高新用户服务的预测准确率，提出一种融合多系统用户信息的协同过滤算法。

Collaborative filtering is becoming a popular one, but traditional collaborative filtering algorithm has the problem of sparsity, which will influence the efficiency of prediction.

协作过滤作为其中一种技术也得到迅速发展，但传统的协作过滤算法存在矩阵稀疏性等问题，影响预测效果。

Today, five approaches are used by recommender systems to generate recommendations, namely knowledge engineering, collaborative filtering, content-based, hybrid and data mine approaches.

推荐方法包括：知识工程、基于内容的推荐方法、协同过滤推荐方法、混合推荐方法、数据挖掘方法。

In order to evaluate our new collaborative filtering algorithm and combined approach, we have developed a Prototype System for Chinese computer science literature automatic filtering.

为了对我们提出的改进的协作过滤算法和结合过滤方法进行评价，我们研制了一个中文计算机科技文献自动过滤原型系统。

The collaborative filtering for the personalized recommendation is by far the most widely used and the most successful personalized recommender technology.

其中，个性化推荐系统中的协同过滤推荐是迄今为止应用最广泛、最成功的推荐技术。

Traditional collaborative filtering does little or no offline computation, and its online computation scales with the number of customers and catalog items.

传统的协同过滤只做很少或不做离线计算，其在线计算量取决于顾客和登记在册商品的数量。

Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog.

与传统的协同过滤，我们的算法的在线计算尺度，独立的客户数量和产品目录中的项目数。

This model of collaborative filtering technology is great help in the mitigation of existing sparse problems and recommendation in time.

该模型的建立对于缓解协同过滤技术中存在的稀疏性问题、推荐的实时性问题有很大的帮助。

Unlike other algorithms, item-to-item collaborative filtering is able to meet this challenge.

与其他算法不同，商品到商品的协同过滤能满足这样的挑战。

A traditional collaborative filtering algorithm represents a customer as an N-dimensional vector of items, where N is the number of distinct catalog items.

传统的协同过滤算法把顾客描绘成商品的N维向量，其中N是登记在册的不同商品的数量。

Thus, this study focused on the collaborative filtering technology.

本文主要针对协同过滤推荐技术展开研究。

Collaborative Filtering(CF) is used for forming recommendation by analyzing the common "taste" shared by a group of customers.

协同过滤技术可以通过分析客户群共同的消费品味来形成推荐。

The traditional Collaborative Filtering has shortcoming as follows: accuracy, data sparse and cold-start.

传统的协同过滤主要存在着：精确性、数据稀疏与冷启动的问题。

Collaborative filtering recommendation algorithm can make choices based on the opinions of other people. It is the most successful technology for building recommender systems to date.

协同过滤是目前最成功的一种推荐算法，它能够基于其他用户的观点帮助人们作出选择。

Our algorithm, item-to-item collaborative filtering, scales to massive data sets and produces high-quality recommendations in real time.

我们的算法，也就是商品到商品的协同过滤，符合海量的数据集和产品量，并能实时得到高品质的推荐。

Unfortunately, traditional collaborative filtering algorithm does not consider the problem of item's multiple contents and often leads to bad recommendation when item has multiple contents.

但由于传统的协同过滤算法没有考虑项目多内容问题，存在项目多内容情况时推荐质量较差。

As you can see from above, it is certainly possible to have a good collaborative filtering system without a recommendation engine (as seen in Flickr).

正如你从上述中所看到的，如果没有一个推荐引擎（如看到的Flickr ）这当然也有可能是一个良好的协同过滤系统。

Regardless of the method, collaborative filtering or inherent properties of things - recommendations are an unforgiving business, where false positives quickly turn users off.

不管用什么方法，协同过滤或基于item相似的推荐都是不会被原谅的商业工具，假阳性般的错误会很快地让用户流失。

Collaborative filtering is one of the most widely used and successful methods for recommendation, which has been made fast development in theoretical research and applications.

协同过滤技术是推荐系统中最广泛使用和最成功的技术之一，在理论研究和实践中都取得了快速的发展。

There are three common personalized recommendatory technologies: information retrieval and extractor, content-based filtering and collaborative filtering, data mining and knowledge discovery.

常用的个性化服务推荐技术包括三种：信息检索与信息抽取、基于内容的过滤和协同过滤、数据挖掘与知识发现。

Recommendation Subsystem is core of the whole system, which implements thesaurus concept hierarchy collaborative filtering algorithm by several steps.

其中资源推荐子系统作为整个系统的核心，主要负责本实验系统中主题词概念分层协同过滤推荐算法的实现。

The key to item-to-item collaborative filtering's scalability and performance is that it creates the expensive similar-items table offline.

商品到商品协同过滤的可扩展性和性能的关键是，它离线建立耗时巨大的相似商品表格。

Google also wanted to leverage the same collaborative filtering technology to be able to recommend images, videos, and music, for which it's more difficult to analyze the underlying content.

Google希望能够通过调节这个复合筛选技术来解决内容分析方法难以解决的图片，video，还有音乐方面的推荐。

They do so using a technique called collaborative filtering, basing suggestions on customers’ previous purchases and on how they rate products compared to other consumers.

他们长期向委托人推荐产品和电影影音软件，并且用一种叫做协同过滤（一种建立在特定消费者之前消费行为以及他们对商品排序基础上的主流技术方法）的手段，基于顾客之前购买商品时提出的建议以及顾客对商品的评价同其他顾客对比情况进行销售。

The key point to grasp about collaborative-filtering software is that it knows absolutely nothing about movies.

对于“协同筛选”软件，把握的关键点在于它对电影一无所知。

A less obvious means of Web personalization is collaborative-filtering software that resides on a Web site and tracks users' movements.

另一个不太引人注意的网站个人化的手段是协作过滤软件，它驻留在网站中，跟踪用户动向。

Realize the clustering algorithm part of the recommendation system based on collaborative filtering and evaluate it.

对基于聚类的协同过滤推荐系统的聚类算法进行了实现和评价。