Awareness Tools

Awareness tools are similar to book recommender systems.  Information filtering systems remove unwanted or duplicated, redundant, information from information streams before presentation to the ususer.  The recommender system predicts ratings or interest orf the user based on information available from the user's profile.  Almost everyone uses recommender systems, such as Amazon's, Pandora's, or Netflix's book recommendations.  Previous book purchases, movie views, or music listened to is accessed by system, which then generates books, music, or movies with similar characteristics.  Many of these systems offer the user feedback opportunities to fine tune their recommendations.

Recommender systems use either content-based filtering or collaborative filtering to generate their recommendations.  Collaborative filtering uses information from the users past behavior to generate recs, the most famous example is Amazon's item-to-item technique, or people who buy x also buy y. The system itself has no understanding of the item it is recommending, it simply relies on the item-to-item transfer.  The problem with collaborative filtering is the cold start, the fact that a new user has no history in the system with which to make comparisons, so the initial data given to users may be sparse. (Schein et. al 2002)

When building a model from a user's profile, a distinction is often made between explicit and implicit forms of data collection.
Examples of explicit data collection include the following:

• Asking a user to rate an item on a sliding scale.
• Asking a user to rank a collection of items from favorite to least favorite.
• Presenting two items to a user and asking him/her to choose the better one of them.
• Asking a user to create a list of items that he/she likes.

Examples of implicit data collection include the following:

• Observing the items that a user views in an online store.
• Analyzing item/user viewing times^[7]
• Keeping a record of the items that a user purchases online.
• Obtaining a list of items that a user has listened to or watched on his/her computer.
• Analyzing the user's social network and discovering similar likes and dislikes. (wikipedia, recommender systems)

 The hybrid approach, which combines the collaborative filterning and the content-based biltering is becoming more popular as it may be more effective in many cases.

The Netflix Prize was a competition put forward by Netflix to improve its collaborative filtering algorigthm called Cinematch.  The competition began October 2, 2006 and within the first few weeks three teams already had bean the Netflix system.  The final prize of one million dollars was awarded on September 21, 2009 to team “BellKor’s Pragmatic Chaos”

The joint-team "BellKor's Pragmatic Chaos" consisted of two Austrian researchers from Commendo Research & Consulting GmbH, Andreas Töscher and Michael Jahrer (originally team BigChaos), two researchers from AT&T Labs, Robert Bell, and Chris Volinsky, Yehuda Koren from Yahoo! (originally team BellKor) and two researchers from Pragmatic Theory, Martin Piotte and Martin Chabbert^.

The team published their algorithm, which is noted in the paper reference below and here:
Andreas Töscher and Michael Jahrer (2009-09-21). "The BigChaos Solution to the Netflix Grand Prize.".

References
•Awareness Tools  http://ipl2.cci.fsu.edu/community/wiki/index.php/Awareness_tools#How_do_Awareness_Tools_work.3F
•Recommender Systems  http://en.wikipedia.org/wiki/Recommender_system
•Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, David M. Pennock (2002). "Methods and Metrics for Cold-Start Recommendations". Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002). New York City, New York: ACM. pp. 253–260. ISBN 1-58113-561-0. Retrieved 2008-02-02.
•http://en.wikipedia.org/wiki/Netflix_Prize
•"Netflix Awards $50,000 Progress Prize in Year Two of Multi-Year, Multi-National Netflix Prize Competition"
•"Grand Prize awarded to team BellKor’s Pragmatic Chaos". Netflix Prize Forum. 2009-09-21.
•Steve Lohr (2009-09-21). "A $1 Million Research Bargain for Netflix, and Maybe a Model for Others". New York Times.
•  Andreas Töscher and Michael Jahrer (2009-09-21). "The BigChaos Solution to the Netflix Grand Prize.".