Google’s Wonder Wheel Experiment

By Vegard Sandvold on March 26, 2009 | Leave a response

Want to participate in one of Google's user interface experiments? Google Blogoscoped tells you how to grant yourself access to the Google Wonder Wheel. Go to google.com, paste the Javascript into the address bar, and take the wonder wheel for a spin. The wheel displays a circle with your keyword, connected to other circles with related terms. There's also a timeline view, and options to show longer snippets and more images for each search result. You can also use filter the results on type (recent, videos, forums and reviews), as well as freshness (time and date). The related terms aren't based on Google's Latent Semantic Analysis (LSI), which you can access by prefixing your keyword with the tilda (~) operator. Google then expands you search with semantically (actually derived from statistics) related concepts. I wonder why they decided not to fuse the two related terms initiatives. (via http://thenoisychannel.com)

Wikipedia - A Democratic Gold Standard for Topic Maps

Wikipedia – A Democratic Gold Standard for Topic Maps

By Vegard Sandvold on March 23, 2009 | 16 Responses

Topic Maps, an ISO standard for semantic networks, relies on authorities to create and maintain Published Subject Indicators (PSIs), uniquely linking single topics to single subjects out there in the real world. TopicMaps.Org has eg. published indicators for languages and countries. But who gets to claim authority over a particular set of topics? Conflicts... Read More »

A Hack Addressing a Flaw - Transparent Recommendations

A Hack Addressing a Flaw – Transparent Recommendations

By Vegard Sandvold on March 10, 2009 | 9 Responses

Transparency and control is not the pinnacle of user experience design for recommender systems. Our ability to scrutinize the recommendations given to us by the system does not unanimously increase our ability to effectively choose the right option. Transparently knowing all the pros and cons of every recommended options may actually decrease our satisfaction... Read More »

When Recommendations Become a Problem

When Recommendations Become a Problem

By Vegard Sandvold on March 2, 2009 | 6 Responses

Some choice good – excessive choice bad. That is the (condensed) Paradox of Choice, according to Barry Schwartz. We need some choice in order to exercise our free will, but the abundance of options we’re facing today (when shopping for groceries, entertainment, education and more) is actually quite overwhelming and paralyzing. No matter how... Read More »

Cake is great, Twitter is good, short URLs can die

Cake is great, Twitter is good, short URLs can die

By Thomas Kjelsrud on March 2, 2009 | 8 Responses

Twitter is becoming increasingly popular and mainstream. We’ll go into some possible and slightly novel uses of Twitter to route the stream of communication to your site or blog. Twitters short message format creates a focused form of communication, but the tight limit enforces the increased use of shortened URLs, through services like bit.ly... Read More »

Four Approaches to Music Recommendations: Pandora, Mufin, Lala, and eMusic

By Vegard Sandvold on February 24, 2009 | Leave a response

ReadWriteWeb gives us some nice examples of the kinds of recommendation systems I wrote about in my previous post. Pandora is content-based, although the features are extracted by humans. The result is high-quality data, but poor scalability. Mufin is a classical example of content-based music recommenders, using a purely algorithmic approach. Lala seems to be old-fashioned word-of-mouth recommendations put on the Internet. eMusic is a hybrid system, but combines social with expert, and social with content-based like Oscar Celma proposes. Apple Genius is most likely a typical collaborative filtering recommender, based on artist (not song or album) similarity.

Four Approaches to Music Recommendations: Pandora, Mufin, Lala, and eMusic

By Vegard Sandvold on February 24, 2009 | Leave a response

ReadWriteWeb gives us some nice examples of the kinds of recommendation systems I wrote about in my previous post. Pandora is content-based, although the features are extracted by humans. The result is high-quality data, but poor scalability. Mufin is a classical example of content-based music recommenders, using a purely algorithmic approach. Lala seems to be old-fashioned word-of-mouth recommendations put on the Internet. eMusic is a hybrid system, but combines social with expert, and social with content-based like Oscar Celma proposes. Apple Genius is most likely a typical collaborative filtering recommender, based on artist (not song or album) similarity.

Does Everything Really Sound Like Coldplay?

Does Everything Really Sound Like Coldplay?

By Vegard Sandvold on February 24, 2009 | 4 Responses

If you have a feeling that all roads somehow lead to Radiohead, you’re not alone. Oscar Celma, my friend and former colleague at the Pompeu Fabra University in Barcelona, successfully defended his PhD thesis on music recommender systems the other day. The thesis, titled “Music Recommendation and Discovery In The Long Tail”,  sheds new... Read More »

All hail the information triumvirate!

By Vegard Sandvold on February 15, 2009 | Leave a response

Wikipedia has come to dominate Google web search results. It often ranks #1 for searches on common topics like Internet and Evolution. Is it true that Wikipedia articles are the very best source of information for all of these topics? Or are we witnessing the effects of a popularity feedback loop, fueled by the principles of least effort, and our tendency to stick with the first and obvious answers? The web link graph is fundamentally a product of socialization, and Google is fundamentally a social search engine. A popularity bias in inherent in all social information systems, leading us all down the same well-trod path. Could it be that, counter to our expectations, the natural dynamic of the web will lead to less diversity in information sources rather than more?

All hail the information triumvirate!

By Vegard Sandvold on February 15, 2009 | 1 Response

Wikipedia has come to dominate Google web search results. It often ranks #1 for searches on common topics like Internet and Evolution. Is it true that Wikipedia articles are the very best source of information for all of these topics? Or are we witnessing the effects of a popularity feedback loop, fueled by the principles of least effort, and our tendency to stick with the first and obvious answers? The web link graph is fundamentally a product of socialization, and Google is fundamentally a social search engine. A popularity bias in inherent in all social information systems, leading us all down the same well-trod path. Could it be that, counter to our expectations, the natural dynamic of the web will lead to less diversity in information sources rather than more?

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