Google announces a new personalized search service. I have noticed ip-address based search results that were carried out by Google over the last year to provide the best results and accomplishment in technology. Yet Google have made surfers aware of the cookie used in personalized search.
What’s the news right, your thinking this has been talked about for years?!
Search gets personal 6/28/2005 03:09:00 PM Posted by Sep Kamvar, Software Engineer, Personalized Search Team.
The truth is search results due to cached ipaddress were being tested for the last four years.
Remember how paranoid folks were in 2003… Don’t be evil?! Big Brother award in 2003. http://www.google-watch.org/bigbro.html And yet the steps to clear search history were all over the web in 2005 http://www.googletutor.com/2005/04/11/clear-search-history/
I got the email below from a colleague saying the following: It remains to be seen if we can still put any credence in SERPs: http://www.youtube.com/watch?v=EKuG2M6R4VM
What’s new in this version of Personal Search?
The cache seems a bit long because, sometimes I search for a mouse for my computer and sometimes a mouse for my elephant. To make this advance in search turely usefull it needs to be more about the context and less about a cache lasting 180 days. People change all the time so youhave to be really quick to be personalized enough. I love the fact that Google all the sudden wants to pull its socks up. It seems they have looked aournd and discovered there are many Google products that need to be explained and launched. Many people still have not even experianced and understodd the power of Google parameters.
Say you want to send Google URLs to a friend but the URL returns different results for different Geographic locations.
example:
Google Query: Firefox http://www.google.com/search?client=firefox-a&rls=org.mozilla%3Aen-US%3Aofficial&channel=s&hl=en&source=hp&q=download+music&btnG=Google+Search
Clean query http://www.google.com/search?q=download+music
So what’s all that extra junk do?example &hl=en means the browser is English &btnG means Google buttonhttp://www.google.com/search?q=download+music&gl=us
rlz=1C1SKPC_enAU344AU344 oops looks like I am from Austraila
One of my favourites &gl=us means show me US results can be applied like this
Wow Kazaa is first for download music or is it? If I do the same search from Google in Australia seems that thepiritebay.org is first. http://www.google.com/search?rlz=1C1SKPC_enAU344AU344&sourceid=chrome&ie=UTF-8&q=download+music
Q.) What does this do? &q=~download+music+-download+music anyone know???
A.) This query looks for synonyms of the term that don’t contain the term. The usual use is google.com/search?q=~download+music+-“download+music”within quotes which will show all the synonyms for a phrase that do not include the phrase it self. Synonyms are show within results in bold. This would include phases and term such as: get music, music, put music, get, put, music downloads, music downloads
Google announced this week that they would start personalising search results, even for users that are not signed into a Google service at the time. It has caused a consistent flow of posts from the blogosphere and quite a lot of comments as well which show that there is a gap in knowledge at some level. Personalisation is not a new concept, it’s been in research for quite some years and there is plenty of literature available on the subject. It will be quite a countinued commitment to stats in order to perfect the formula. The system will only improve as we make the effort to be part and parcel with the development semantic web. Theory will come into practice only through the use of mankind so we must continue to click about and build the sematic web together.
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YouTube DirektPersonalized Search for Everyone
Today we’re helping people get better search results by extending Personalized Search to signed-out users worldwide, and in more than forty languages. Now when you search using Google, we will be able to better provide you with the most relevant results possible. For example, since I always search for [recipes] and often click on results from epicurious.com, Google might rank epicurious.com higher on the results page the next time I look for recipes. Other times, when I’m looking for news about Cornell University’s sports teams, I search for [big red]. Because I frequently click on www.cornellbigred.com, Google might show me this result first, instead of the Big Red soda company or others.
Previously, we only offered Personalized Search for signed-in users, and only when they had Web History enabled on their Google Accounts. What we’re doing today is expanding Personalized Search so that we can provide it to signed-out users as well. This addition enables us to customize search results for you based upon 180 days of search activity linked to an anonymous cookie in your browser. It’s completely separate from your Google Account and Web History (which are only available to signed-in users). You’ll know when we customize results because a “View customizations” link will appear on the top right of the search results page. Clicking the link will let you see how we’ve customized your results and also let you turn off this type of customization.
http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html
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Free resource papers:
System and methods for personalized search, information filtering and for generating recommendations utilizing statistical latent class models by Thomas Hoffman and Jan Puzicha (Reccomind, inc – Berkeley)
Enhancing Collaborative Web Search with Personalization: Groupization, Smart Splitting, and Group Hit-Highlighting by Meredith Ringel Morris, Jaime Teevan, Steve Bush (Microsoft Research, Redmond, WA, USA)
Task aware search personalization by Julia Luxemburger, Shady Elbassuoni & Gerhard Weikum (Max-Planck Institute of Informatics)
Personalized Concept-Based Clustering of Search Engine Queries by Kenneth Wai-Ting Leung, Wilfred Ng, and Dik Lun Lee (Hong-Kong University)
Discovering and Using Groups to Improve Personalized Search Jaime Teevan, Meredith Ringel Morris Redmond, Steve Bush(Microsoft Research)
Cluster Based Personalized Search by Hyun Chu, Lee and Allan Borodin (University of Toronto)
Personalized Web Search by Gossiping with Unknown Social Acquaintances by Marin Bertier, Davide Frey, Rachid Guerraouiz Anne-Marie Kermarrec, Vincent Leroy (INRIA)
Rank Optimization of Personalized Search by Lin LI, Zhenglu YANG, and Masaru KITSUREGAWA (University of Tokyo)
To Personalize or Not to Personalize: Modeling Queries with Variation in User Intent by Jaime Teevan,Susan T. Dumais & Daniel J. Liebling (Microsoft Research)
Learning user interests for a session-based personalized search by Mariam Daoud, Lynda Tamine-Lechani & Mohand Boughanem (Institut de Recherche en Informatique de Toulouse)
