compare text similarity online

Part 2nd: In-depth recommendation of engine-related algorithms-collaborative filteringThe first article in this series provides an overview of the recommendation engine, and the following articles provide an in-depth introduction to the recommended

Http://yidianzixun.com/n/09vv1FRK?s=1Completely excerpt from the Web page1 Collective intelligence and collaborative filtering 1.1 what is collective wisdom (social computing)?Collective Wisdom (collective Intelligence) is not unique to the Web2.0

Full-text retrieval system for machine translationAbstract: This article introduces the design and implementation of a full-text search system for machine translation.Bucket structure and common full-text retrieval system functions such as boolean

Transferred from: http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy2/index.htmlThe first article in this series provides an overview of the recommendation engine, and the following articles provide an in-depth introduction to the

From: http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy2/index.html For innovative companies in 2005, the most important revolutionary idea may be the so-called "Long Tail" theory proposed by Chris Anderson, editor-in-chief of Wired

The following is a paper note, in fact, mainly excerpt, this piece of doctoral dissertation is logical, layers in depth, so I keep more. See the second chapter, I found in fact this piece of article for me More is science, science Bar ... First, the

The Mission's vision is to connect consumers and businesses, and search plays a very important role. With the development of the business, the number of businesses and group buying in the United States is growing rapidly. In this context, the

One:Recommendation System tasks: Contact users and information, on the one hand, help users find valuable information on their own, on the other hand, the information can be displayed in the presence of users interested in it, so as to achieve the

Background and significance In the Web2.0 era, especially with the popularity of social networking sites like Flickr and Facebook, images, videos, audio, text and other heterogeneous data are growing at an alarming rate every day. For example,

Our problem is such a M-item, m-user data, only some users and some of the data is scored data, the other part of the score is blank, at this time we want to use the existing part of sparse data to predict the gap between the items and data between