This is the final version.
An Analysis of Collaborative Filtering Systems
Privacy and Trust are major issues in promoting corporate portals' functions-gatherig, sharing and disseminating of information. Those issues are also related to all topics of KMS.
This article provides "new non-third party mechanisms to overcome" the barriers against privacy and trust, and also solutions for "finding shared preferences, discovering communities with shared values, removing disincentives posed by liabilities, and negotiating on behalf of a group" ,and techniques "to enable these new capabilities".
I found more specific information about the Knowledge Pump Sytem which we learned in "collaborative flitering" class.
The Knowledge Pump can foster an evironment that encourage the flow, use and creation of knowledge by supporting social network and electronic repositories.
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"askOnce is a personal 'single click' access point for finding exactly the information that you need. askOnce is a web-based meta-search application that allows you to search multiple repositories and data-types with a single query. Once learners have identified texts or multimedia documents that seem interesting to them, they can post them onto KP recommendation services."
The integration of two technologies could accelerate collaborative filtering.
The collaborative filetering made " the butterfly effect", which means tiny difference in the initial conditions becomes amplified by the evolution.
"One flap of a seagull's wings would be enough to alter the course of the weather forever."
In the article, The Science of the Sleeper, How the Information Age could blow away the blockbuster, Mary Gay Shipley's recommendation functioned like that by making a unknown novel a best-seller.
The article reminds the significance of customized filtering in the information age when readers are starved for finding their doppelganger.
Among Primary readings of Week 11, the following readings have been damaged, so I couldn't link to them now.
But, we can find it on the "Online Journal" in UTNetCat.
These are the URLs:
1)Using Collaborative Filtering to Weave an Information Tapestry
http://infotrac.galegroup.com/itw/infomark/965/352/33929805w1/purl=rc1_EAIM_0_A13039895&dyn=7!xrn_1_0_A13039895?sw_aep=txshracd2598
2)Fab: content-based, collaborative recommendation
http://infotrac.galegroup.com/itw/infomark/965/352/33929805w1/purl=rc1_EAIM_0_A19284346&dyn=14!xrn_1_0_A19284346&bkm_14_1?sw_aep=txshracd2598
I saw this from Wired News today:
Service Keeps Music Files Humming By Katie Dean
It describes an open-source, community-run site, MusicBrainz, that provides metadata for audio files. It's also going to provide some peer-to-peer recommendation features.
Some call Collaborative Filtering - "Recommendation Systems" or "Recommender Systems". This Workshop includes many of the people who have been working in this area. Note that previous workshops may provide great CF insight as well. Recommender Systems Workshop 2001 - Workshop Notes