Time ago I received the request to republish one of my paper in the book “Internet Search Engines – An Introduction“. So I took the chance to extend my paper “Page-reRank: using trusted links to re-rank authority” from 4 to 10 pages and cordially give permission to include it in the book.
The publisher is ICFAI University Press which of course is not Oxford Press; it is an publisher for Indian Universities and in fact after publishing I received few emails from Indian students.
Anyway what I’m more proud of is that I have a Creative Commons released paper published on a book! When they asked me to publish it, I put this as condition and they said “yes”. Since I tried many other times to amend the copyright form publishers ask you to sign before publication (in general it basically says “you give us all the rights”) with something a bit more liberal such as a Creative Commons license, I’m very happy about this, about the license.
The license is a Creative Commons Attribution-Share Alike 3.0 License so you can legally do whatever you want with the paper as long as you cite me and share what you produce with the same license.
Anyway in the book I’m in good company: there is also a paper by Prabhakar Raghavan, head of Yahoo! Research “Using PageRank to Characterize Web Structure” and one by Ricardo Baeza-Yates, director of Yahoo! Research labs at Barcelona “Pagerank Increase under Different Collusion Topologies”.
This post is also an excuse for starting my blog on Nature.

Following there is the summary of my paper as it appears on the book, but you can also download the paper from my site.

The tenth article titled “Page-reRank: Using Trusted Links to Re-rank Authority” by Paolo Massa, highlights that the present HTML linking mechanism does not allow the author of a web page to express the endorsements of its content. Consequently, algorithms like PageRank produce rankings that do not capture the different intentions of web authors. The authors explore the possibility of adding simple semantic extensions to the hyper linking mechanism, by using a large real world data set and demonstrate the different page rankings produced by considering extra semantic information in page links. The paper concludes that by adopting (programming) languages that allow authors easily encode simple semantic extensions to their hyperlinks, the web (or search) intelligence can be optimized to pull relevant pages for a given search query.