The research we do at Helioid involves a lot of reading. With some notes and summaries included, here is a list of the literature we’ve focused on from 2008 to 2009:
Machine Learning
G. Lebanon, Y. Mao, and J. Dillon. The Locally Weighted Bag of Words Framework for Document Representation. Journal of Machine Learning Research 8 (Oct):2405-2441, 2007.
The simple answer to how Helioid benefits users is that Helioid represents information and information navigation in a more efficient manner. This gets a complex when looking at how each individual uses the internet and searches for information, but still the core is the same. A current issue with web search, as Google’s Marissa Mayer explains, is that it is undeveloped and not advanced, “Think of it like biology and physics in the 1500s or 1600s: it’s a new science where we make big and exciting breakthroughs all the time.”
Continue Reading...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.
Continue Reading...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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