Matthew Syrett of PBS Media Shift recently shared his thoughts on the flaws of Facebook’s graph search. He writes, “With Graph Search, Facebook intends to mine our social networks to unlock knowledge stored among our online friends to create a recommendation search engine. Instead of crawling the Internet to build search indices, Graph Search will use our social media connections, likes, and Check-ins to make search indices to respond to our queries. Using Graph Search, we should be able to find restaurants that have been checked into or liked by our Facebook friends. We can discover movies based upon the likes of our connections, or relevant music. The project strives to be an alternative to everyday search queries we all do to discover things to do or get in our lives — all validated by people we know and trust, and not by unknown reviewers or a faceless algorithm.” Read more

Lenna Garibian of MarketingProfs.com recently discussed a new eye-tracking study that monitored how users interact with Facebook graph search compared to other search engines. She writes, “Similar to search engine results pages, being in the top two or three listings of a Facebook Graph Search results page is key to ensuring a listing is seen quickly and for a relatively long duration, according to research by Mediative, which used eye-tracking technology to explore which elements of the Facebook Graph search results page hold the most interest. For the study, 21 people were asked to view the results of the query “restaurants nearby” on a given Facebook page. Among the participants, 95% focused on the top two results of a Facebook search results page—and the proportion of viewers who viewed a given result decreased fairly uniformly as viewers scanned down the page.” Read more

The semantic technology platform behind restaurant dish discovery service Dishtip (which The Semantic Web Blog discussed here) has made its way to a new domain: mobile apps. The company last week unveiled AppCrawlr, which uses its TipSense content discovery and knowledge extraction technology to cut through the noise to help users find the app that’s right for them in a world of hundreds of thousands of options for iPhone, iPad and Android devices.

“With traditional search models there’s no easy way for guided discovery to narrow down from all the apps out there to what you want,” says Dave Schorr, who with Joel Fisher is a co-founder of TipSense LLC. Keyword searches aren’t going to help you find apps that help when you are having a bad day, for instance, or understand that someone looking for a dating app (as in relationships) is looking for something different than someone looking for a date (as in scheduling and productivity) app. But searches on AppCrawlr can suss those out, taking data from from all across the web – blogs, tweets, reviews, and so on – and surfacing and organizing the concepts and topics buried in all that unstructured data.

“It’s a new paradigm to manage a large data set,” says Schorr. “We’re using concepts to come up with a much better experience for discovery.”

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Photos your friends took in New York City. Restaurants in Chicago your friends have been to. People who like running and who live in Denver, Colorado. Friends of friends who are interested in ballroom dancing or hiking.

Facebook’s new Graph Search promises to find all those things, and more, for you. Mark Zuckerberg announced the new way to get really personal in your searches for people, photos, places and interests today at an event at the company’s headquarters in Menlo Park, Calif., and the company has put up an explanatory page on the new service here.

While it doesn’t mention Open Graph specifically, the protocol that lets apps model a person’s activities based on actions and objects, it makes sense that the app-specific actions it lets people share on Facebook are feeding into the new search feature.

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