SeeqPod? It lets you search for music. It finds playable content (songs and music you can play instantly without having to sign up or register) and lets you create a ipod-like-play list of the same, which is impressive at first, making it easy and fun to use SeeqPod. Like all other search engines, SeeqPod has crawlers out in the WWW searching and indexing online content. This progress of this indexing process can be witnessed on their homepage. Quoting RWW – ‘The homepage is reminiscent of Google's original unadorned page with just a simple search form. The vital difference is that SeeqPod also displays a sample of current music being indexed by its engine. These songs are meant to draw you in, and succeed at it. There's something mesmerizing about watching track after track scroll by’.
Not very innovative is what you might think. Looks like a mash-up between ipod and Google. In fact that’s what many online blogs quote. But there is more to SeeqPod than being another Google rip-off.
SeeqPod's ‘discover’ feature:
Before I explain how this works, let’s look at what developers of SeeqPod have to say about this feature.
“We have created a totally unique algorithm that finds the hidden relationships between playable topics, not unlike the way our minds make relevant associations between subjects of interest to us. Our technology mines the deepest crevices of the Web, returning useful, precise results.
Our Discovery engine generates related search results for the query entered in the search box. Discoveries are powered by our patented language-independent algorithms that analyze and mimic the way people make associations in everyday life.”
So SeeqPod tries to build a correlation between a particular search topic and related articles on the web. The algorithms responsible to do this works in the same way that you or I would relate an artist with a particular object or topic on the web. A good example of this would be searching for an artist and finding other artists who have similar styles or maybe share a band member. How does SeeqPod build this understanding? Don’t have a clue!!! (As with all proprietary work, this one too keeps it secrets locked behind a veil of secrecy).
Testing SeeqPod:
We are concerned with the discover feature, as this is what provides ‘recommendations’ to us for a given topic. Let’s go with my favorite artist Audioslave.
Search throws up 179 ‘playable media’ results and 114 discoveries. First on the list of discoveries is ‘Good Charlotte – the young and the hopeless’. I can’t explain this result. But a few results down, I see Soundgarden – burden in my hand. The current lead vocalist of Audioslave, Chris Cornell, was once on the lead vocals for Soundgarden.
In totality, the results do make sense. Artist who have a history with Audioslave, songs that have similar characteristics to what Audioslave plays, and of course links to merchandise, all make for good discoveries.
I can definitely find all this related stuff on Google too, but I would have to extend my search query to reflect what I want. SeeqPod throws up these recommendations even if you don’t ask for them. I found a Audioslave t-shirt which I would love to buy, even though I was not looking for one. This shows how this technology helps you ‘discover’ more about a topic or artist or album.
Seeqpod does a good job of organizing its search results and presenting the relevant content to its users. By not hosting any music or video files, its neatly avoids the ire of RIAA and other DMCA adhering record labels. What makes Seeqpod interesting to use if the fact that it provides instant replay of media, and provides some useful recommendations about the same without taxing the user by asking to him search with a more specific intent. With Seeqpod you would definitely not find conventional results at the top of your list. The Audioslave homepage was no where to be found in the SeeqPod search results, while Google lists it at the top of its finds. Seeqpod also lets you help it find more relevant content by letting you submit sites which you think might be relevant to a particular topic.
There are a number of ways to try to recommend items to a user. But the building a correlation model which emulates the way a human would link objects is interesting and can be applied to a lot of business models to simulate the human thinking process.
There are a lot of unanswered questions …like what factors decide the ordering of the search results? Does Seeqpod consider user hits and other usage statistics while recommending a particular page/item? Its internal working and algorithms would definitely be interesting to explore.
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