WebProNews

Tag: Social Graph

  • RentSocial: The Facebook Of Apartment Hunting

    I currently live in an apartment complex. My lease ends in July and I’m already on the hunt for a cheaper place closer to where I work. I’ve been using those clunky apartment finder Web sites and they just don’t do it for me. RentSocial says its here to make my life easier.

    RentSocial is a brand new startup – its 87 likes on Facebook confirm that – that aims to make apartment hunting more like sharing where you’ve been on Facebook. It connects with your Facebook social graph to share places with friends and perhaps find potential roommates looking at the same property. It also has the standard amenities of current apartment Web sites by providing reviews and information on the property.

    To test this out, I began to look for my current apartment within their system. RentSocial uses Google Maps to locate your current living arrangement, but unfortunately my property is not listed. Why is that? GigaOM says that RentSocial is “coming to market with eight of the top 10 property management companies in the U.S.” My place is apparently not owned by one of those or is one of the two companies not listed yet. I did find four properties in Lexington, KY, however, and checked out one of the properties listed.

    RentSocial: The Facebook Of Apartment Hunting

    RentSocial lists all the relevant information that you would find on an apartment properties Web site with a list of amenities, price range, number of bedrooms and baths, etc. It also features the floor plans and office hours. As you can see though, this listing is kind of a ghost town. We’ll chalk that up to not many people knowing about it yet.

    People who already live at the property can leave a review of the place, share the apartment with friends, confirm that they live there and other things to make the process easier for others looking for a place to live. The “I live here” function is especially useful because anonymous reviews of apartments is the one thing I absolutely hate about apartment finder Web sites. Being able to attach a face to a review makes it much easier to sort out the real reviews from trolls that “zero bomb” apartment listings.

    There’s another side effect of listing your current residency at an apartment. Once the service grows, people will be able to see who lives at their apartment to, as GigaOM puts it, “develop a sense of community within a given building.” As much as my apartment wants me to hang out with other people living there, I’m not going to hang out with strangers. Getting to know who they are online first makes that much easier.

    As stated, the service is just beginning. There’s not a lot of options yet especially for those people not in large Metropolitan markets. Once more people begin to use the service, it could be a fantastic alternative to the usual apartment finder Web sites that I loathe so much.

  • Implicit Social Graphs Rise From Interest Not Location

    There has been a ton of conversation about social graphs, location and photo sharing lately. Most of this conversation has arisen due to the release of the photo-sharing application Color. The photo-sharing part of the discussion is only a gateway to the larger discussion of social graphs. I have been looking at the basic location-based applications a lot as well. Facebook Places, Google Places, and Foursquare are all fighting for the location crown. Granted, location-based applications are becoming very popular, but they were quickly outpaced by the group discount services like Groupon and Living Social. There is a very good reason for this as well. Location alone does not denote interest or intent. This is also why implicit social graphs are so important, they are based on interest or intent. Group discount services have a location aspect, but they are driven by interest and intent. All of the people involved in buying a discount have interest in what they are purchasing.

    This is also why Color’s launch was received with such mixed reviews. They are trying to use location as the implicit social graph, but there is no real interest. Even if there are several people at the same location, the location may denote different purposes. As an example, if I am at a restaurant I probably do not want the interruption that would come with a location based graph. However, if I am visiting a tourist attraction I may welcome the location-based interaction, mainly because there is implicit interest in the location.

    Fred Wilson has a very good description of how he was introduced to implicit graphs:

    My first experience with this sort of implicit social graph came almost six years ago via my musical neighbors graph at last.fm. I don’t think I actually know any of these people in real life, but they are the last.fm users who have the closest taste to mine in music, right now. That right now is important because my musical neighbors graph looked differently last year and will look differently next year.

    Obviously, people’s musical tastes change over the years, so a static graph is not entirely useful. The “musical neighbors” concept and Pandora’s music genome concept allow for discovery because they are using similar interests to create the implicit graph. A person’s friends may not have the same musical tastes and thus would actually clutter the listening experience with music the listener is not interested in.

    In addition to the creation of implicit graphs, sometimes there needs to be a conversion from the implicit to the explicit graph. Colin Walker talks about this in reference to sporting events and other interest-based interactions:

    Repeated interactions within implicit graphs can lead to a bleed from the implicit to explicit – once you get to know them some of those from implicit graph become ‘friends’ and, after a while, can be invited over in to the explicit graph.

    This bleeding complicates the structures of explicit graphs because these new “friends” are not initially the same type of friends as those people you have known for years. Then there are some people in your explicit social graphs that you lose contact with. Even though they could have been friends previously, differing interests and the effects of time can change their relationship to you. This is where the concepts behind Color become interesting. Obviously, there needs to be some flexibility or “elasticity” in your social graphs, regardless of whether the graph is explicit or implicit. Color CEO Bill Nguyen had an interesting comment about this in a ReadWriteWeb article:

    In the world of Facebook, once someone is your friend, they’re your friend until you return and re-evaluate that relationship, regardless of whether or not you’ve ever spoken to them again. In reality, the relationship could have fizzled long ago, yet it’s still a bond as good as any. With Color’s “elastic” social graph, these ties can fade and disappear. Color’s ability to accurately determine location and user proximity is what makes this sort of social graph – an implied, impermanent and elastic social graph – even possible.

    As I previously stated, I do not agree with the importance of location in this quote, but the general concept is important. Location and time can both be an attribute of the implicit social graph, as can be seen with the SXSW conference. Just because you are in Austin does not mean that you share the social graph with a bunch of people. However, if you happen to be near specific locations in Austin during the same time as the SXSW conference and you have previously shown interest in web and technology startups, then you would be part of the same implicit social graph. Without the interest part of the equation, you could become part of the implicit graph purely by coincidence, maybe you work or live in Austin.

    Om Malik has an excellent post this morning that really hits the same points, but talks about them differently. He mentions “happiness” and “utility”:

    One of the reasons Instagr.am works is because it has that “happiness” attached to it. When I see my friend’s baby boy, it brings me joy. I see Mathew Ingram at an ice hockey game; it makes me warms my heart to see him enjoying time with his family. I reward Instagr.am with my attention because it makes me happy. That is its utility.

    His examples and Fred Wilson’s examples are excellent reasons why some social applications really work well and others don’t get traction.  Implicit social graphs are really driven by interests where time and location can be attributes of that interest but they are not the primary definition of that interest.

    Originally published on Regular Geek

     

  • Facebook Lets Site-Owners Target Content Based on Individual “Likes”

    Did you know you can publish content directly to people that click the "like" button on any piece of content on your site?

    A recent post on the Facebook Developer blog discusses just that. "As part of Operation Developer Love, we are are continuing to update our documentation," said Facebook’s Ankur Pansari. "Recently, I was talking with some developers in New York, and they were surprised to learn that they could publish updates to people who have liked their Open Graph Pages."

    "You can publish stories to people who like your Open Graph Page the same way you write a Facebook post from your own wall. The stories appear in the News Feeds of people who have clicked the Like button on the Open Graph Page," he added. "You can also publish using our API. If you associate your Open Graph Page with a Facebook app using the fb:app_id meta tag, you can publish updates to the users who have liked your pages via the Graph API."

    Do more with the Facebook like button A tip of the hat goes to Josh Constine at InsideFacebook for pointing this out, as well as raising a good point that publishers should consider: "For instance, retailer Urban Outfitters has Like buttons on every product in their website’s ecommerce store. It sells a wide variety of products, from clothing to bikes. If the Urban Outfitters Facebook Page posted an updated about a new line of bikes it was carrying, only a small part of their audience would find it interesting, while a large portion of their audiences would find the update irrelevant or even spammy, leading them to click the Unlike button."

    "Instead, Urban Outfitters could publish the update about bikes to only those users who’ve clicked Like buttons on their bikes," he adds. "By sending product-specific updates to those who Like that type of product, Urban Outfitters can send higher relevancy updates more frequently but to less people, increasing click through rates and driving more traffic to their website without spamming all 600,000 fans of the Facebook Page."

    Basically, this has the potential to be a very powerful tool for anyone using "like" buttons on their sites, but like any other powerful tool, handle with care.