Home Summize: Enhancing Search With Heatmaps

Summize: Enhancing Search With Heatmaps

We wrote about Abdur Chowdhury in our recent
of the Open Data 2007 Workshop. Until recently he was running AOL Search,
but left to start a new venture. Abdur is an impressive thinker and articulate speaker,
with a great track record in the industry. So we decided to take a look at what he is up
to! As it turns out, he is developing an innovative vertical search engine for
called Summize. This search engine
uses a technique called heatmaps, which we’ll explore in this post.

Heatmaps Primer

A heatmap is a visualization technique for
displaying complex information in two dimensions, using colors. For example, heatmaps are
used in weather maps to display temperature or amount of rain. The coloring works like
this: you choose a range of values (for temperature on the chart to the right, the range
is from -60 to 120 F) and then map colors onto this range. There various ways of doing
this mapping, depending on how fine grained you need the map to be. In the case of just
two colors, say red and green, you can do it using RGB encoding – starting with red
moving towards black, and then passing black towards green.

Heatmaps are also actively used in biology, particularly in DNA microarrays. Recently,
stats service CrazyEgg applied heatmaps to tracking
what visitors do on a user’s website. Their software captures user clicks on each page
and then presents a summary in the form of a heatmap. The visual effect is stunning – you
get instant insight as to what your visitors are doing on the site.

Summize – applying heatmaps to search

Just as CrazyEgg’s application of heatmaps to click analysis is clever and useful, so
is Summize‘s way of applying heatmaps to shopping.
As with any search engine, you key into the search box what you are looking for. Summize
searches its database and comes back with the results. It is the presentation of the
results that makes this search engine very different. We see a summary of all product
ratings presented as a heatmap, plus a digg-like voting mechanism and product thumb
previews presented – all in one clean and simple interface.

Summize’s infrastructure crawls user reviews all over the web and accumulates them
together, into a single normalized rating database. The search results show the votes
from this database using the heatmap. For example, in the figure above we see that the
sum total of opinions of the Canon Powershot is 60% – very positive. Only 9% are
quite negative and the rest are in-between. So this is essentially an instant
recommendation for the family of products. 

Beyond that, we get details for each matching product – including a product-specific
heatmap. To get reviews of each product, you can click the product link.

Digging deeper into Summize

Beyond informative heatmaps, Summize does a good job with other search result
essentials. Firstly, it has three sorting knobs – by relevance, by red (bad) and by green
(good). It also uses Ajax to show product previews on rollover – which is elegant,
unobtrusive and useful. Another interesting feature is the voting up or down, which
allows Summize to collect its own voting information. This looks like the beginning of a
home-grown review system, to supplement the crawler results. 

Finally Summize shows you the price next to each item, on Amazon. At the moment, the
service is only affiliated with Amazon and offers Ajax popups to buy the item from
Amazon. From the way the popup is designed, we can deduce that other sites will be
integrated in the future.

Comparing products using Summize

Comparison shopping is an essential part of shopping and product research. Summize
uses heatmaps to compare products. Again, the ‘a picture is worth a thousand words’
wisdom proves true. Seeing the heatmaps side by side gives the consumer a quick verdict
on what other consumers collectively voted for:


Summize is impressive. Its power is in delivering a lot of information, using a
relatively simple interface. And , like the Metacritic movie review aggregator which we
earlier this year, Summize brings together reviews and normalizes ratings to output
essentially a single rating number. 

The presentation is not only innovative, but useful. One thing that could hurt the
adoption somewhat is the fact that there is a substantial percentage of the population
with various forms of color
. But to address this issue, Summize could develop a version that uses

Overall Summize, which is still under the radar, looks promising. Please take a look
at it and let us know what you think.

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