January 23rd, 2009
At Helioid, we strongly believe that the future of the world wide web is experiencing a transition from minimal 2D interaction towards immersive 3D interaction. The constrained navigation offered by current browsers is outmoded and outdated. The popularity of the Wii and iPhone demonstrate that if users are given improved alternatives to the classic styles of interaction they will make use of them. Immersive 3D environments are an improved alternative for web browsing.
The undercurrents of modern innovation hold a revolutionary concept in information interaction. Helioid is dedicated to promoting this revolution.
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Tags: cooliris, visualization
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January 17th, 2009
In early December Microsoft Live Labs released Thumbtack, which is said to: “[use] machine learning and natural language techniques to understand the information you give it.” Looking through the interface one notices some interesting tools. Such as a gadget that creates plots based on attributes of the items you collect and a “Layout Gadget” that I assume creates layouts but currently appears to only work with IE7. Intelligent parsing of information, on demand analysis, visualization, there are great ideas here. The unaccomplished obstacle is how to allow users access to these in an intuitive and simple fashion.
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Tags: microsoft, personalization, visualization
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January 16th, 2009
A little over a week ago, TechCrunch featured an article on the latest round of funding raise by rising star in web search, Kosmix. In said latest round, Kosmix managed to rake in an impressive $20 million from a wide range of investors, led by Time Warner, bringing the search engine’s total funding to $55 million. Upon paying a visit to their site, it becomes immediately apparent what all the hoopla’s about. Kosmix pulls the top search results from a variety of popular sources in a variety of different categories, including video sites YouTube and Truveo, info sites like Wikipedia and HowStuffWorks, and shopping sites like Amazon and Ebay, in order to create a mash-up of all the possible kinds of information you might be interested in. A collection of related subjects are also presented in the left margin of the results page, in order to facilitate some degree of search refinement. Without a doubt, Kosmix provides a search experience quite distinct from the major search engines, and I feel fairly safe in saying that most searches performed with Ask would yield more fulfilling results with Kosmix. And yet, after playing with Kosmix for a while, I felt as though something was amiss.
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Tags: kosmix, refinement
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December 5th, 2008
This articles concerns a method for evaluating the fitness of content topic models and document topic models based on the dissonance between a set of documents and the set of documents generated by composing inverse functions and applying them to the original set of documents. A document generating function is applied to a topic generating function that is applied to the original set of documents. In order to compare topics, one can look at the original set of topics compared to set of topics generated by apply a topic generating function to the documents generated by applying a document generating function to the original set of topics.
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Tags: learning, models, refinement
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December 3rd, 2008
Google recently released their new SearchWiki feature which allows users, who are logged into a Google account, to rearrange search results (by clicking on arrows that move them up or down one slot), remove results from the returned list, and comment on results (all comments are made public). More information is in this Google blog article.
It’s encouraging to see Google taking user responses into account. It has always been our opinion that this is something sadly missing from the mainstream search world. Google also states that the results’ movements, removals, and comments will not be used as input to their search algorithms. Well, at least not yet.
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Tags: google, personalization
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November 25th, 2008
Google’s failure to present the user with relevant results stems from an inability to capture user feedback and implement a method for users to provide feedback. The failure in part stems from the assumption that a user wants to go through their search results in a linear fashion.
Here are a couple ways in which Helioid’s non-linear approach to searching helps solve this problem.
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Tags: learning, models
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May 27th, 2008
Last night I drifted off to sleep thinking about scientists and online collaboration. I’ve been thinking about these things as I drift off to sleep much more frequently than usual since I read the chapter on “social information foraging” in Peter Pirolli’s Information Foraging Theory. In said chapter, Pirolli describes a number of studies of trends in large groups of specialists working towards a common set of goals, and the degree to which such communities of specialists collectively aid their individual members in making contributions to meeting said goals. The subjects explored within a few of these studies that really caught my eye were the use of co-citation analysis to visualize a field of study or network of specialists, and, as Pirolli puts it, the “brokerage of structural holes” in these networks. The former of these I was familiar with, as the technique’s been pretty well explored from a variety of angles, but I had never seen the latter presented the way in which Pirolli does.
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Tags: collaboration, pirolli
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May 27th, 2008
A debate rippled across a few tech blog sites following Erick Schonfeld’s reiteration, a few weeks ago, of some claims made by Nova Spivack concerning the fate of traditional keyword search. As Schonfeld explains, Spivack is of the opinion that as the number of web pages a search engine has to sift through explodes exponentially, the efficacy of a simple keyword search will drop off. Spivack himself explains the problem as follows:
“Keyword search engines return haystacks, but what we really are looking for are the needles. The problem with keyword search such as Google’s approach is that only highly cited pages make it into the top results. You get a huge pile of results, but the page you want—the ‘needle’ you are looking for—may not be highly cited by other pages and so it does not appear on the first page. This is because keyword search engines don’t understand your question, they just find pages that match the words in your question.”
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Tags: keyword search, semantic web
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