JabRef reference manager is a Java-based tool to manage your BibTeX references. “JabRef runs on the Java VM (version 1.4.2 or greater), and should work equally well on Windows, Linux and Mac OS X.”

(Thanks to Harold Boley for pointing it out.)

A pretty exciting article in the New York Times about what is “talent”. The short story is that you should practice hard while ensuring you have immediate feedback and have clearly defined goals.

when it comes to choosing a life path, you should do what you love — because if you don’t love it, you are unlikely to work hard enough to get very good. (…) what they really lack is the desire to be good and to undertake the deliberate practice that would make them better.

(..) there is surprisingly little hard evidence that anyone could attain any kind of exceptional performance without spending a lot of time perfecting it.

(…) Students should be taught to follow their interests earlier in their schooling, the better to build up their skills and acquire meaningful feedback. (…)

(…) two key elements of deliberate practice: immediate feedback and specific goal-setting.

(Got it from Unreasonable.)

Jedox will release a free open source Linux MOLAP server by the end of the year. A pre-release of the software is expected by mid of 2005.

All data is stored entirely in memory. Data can not only be read from but also written back to the cubes. Like in a spreadsheet, all calculations and consolidations are carried out within milliseconds in the server memory while they are written back to the cube.

I sure hope that by “memory” they include “external” memory because otherwise, their cubes are going to have to be quite small. Normally, you’d at least memory map large files as Lemur OLAP does.

Slope One in Automated Collaborative Filtering
By
Bo Xu

Examining Committee:
Supervisor(s):            Dr. Huajie Zhang
                          Dr. Bruce Spencer
Chairperson:              Dr. Mike Fleming
Internal Reader           Dr. Yuhong Yan
External Reader           Dr. Donglei Du                                                                       

Thursday, May 18, 2006
10:00 a.m.
IT-C317 (Information Technology Center)

ABSTRACT

We focus in the Collaborative Filtering field on how
to improve the quality of the prediction and
recommendation and how to improve  the
performance on large datasets. Only rating-based,
non-sequential problem space is discussed. A
detailed survey of current technologies which
might be used in recommender systems is
presented to the readers, followed by a thorough
analysis of a newly proposed algorithm by
Daniel Lemire and Anna MacLachlan: Slope One.
To test the family of the Slope One schemes'
prediction quality as well as their performance,
experiments are conducted by means of
comparing them with other representative,
up-to-date collaborative filtering and machine
learning algorithms. The results show that
Slope One, in spite of its simplicity, is an
efficient, accurate within reason, and scalable
Collaborative Filtering algorithm and therefore
especially applicable for online E-Commerce
recommender systems.

ALL GRADUATE STUDENTS ARE ENCOURAGED TO ATTEND

*********************
Linda Sales
Administrative Assistant
Faculty of Computer Science
University of New Brunswick
540 Windsor Street
Fredericton, NB
E3B 5A3

Phone: 506-458-7285
Fax: 506-453-3566

In Information Retrieval, you can’t have both great recall and great precision, so you have to balance the two. What are the possible criteria to pick the best recall/precision?

What I found so far, on wikipedia of all places, is the so-called F-measure or balanced F-score, and it is merely the harmonic mean of the recall and the precision. This seems to have almost no theoretical foundation?

Anyone out there has proposed an exciting way to pick your recall and precision?

I don’t want to be too critical of the field of Information Retrieval, but sometimes, when I read papers from the 1950′s, it feels like they knew everything there was to know. I sure hope that people came up with more exciting ideas than just “use the harmonic mean” to pick the best recall?

Update: I found this related paper:
Cyril Goutte and Eric Gaussier, A Probabilistic Interpretation of Precision, Recall and F-score, with Implication for Evaluation, but it is only a partial answer to my question.