Over the past few days we have seen Twitter get criticized heavily over the #fixreplies fiasco. Much of the complaining was due to the communication of the changes, or the lack of timely communication. However, there was another large group of voices that used the @replies as a way of discovering other interesting users. This is important because there really is no way to find new users on the service.
You spend a lot of time creating great content for your site, but are you ever curious about which parts of your website your community likes best?
OK, the connection may not be obvious to many people, but what is recommendation? Recommendation is really just a form of discovery that has additional variables. For the recommendation widget, users may be able to “discover” new content on your blog because other people have liked it. The idea here is that the users in the community have similar tastes, given they are reading the same blog, and probably like some of the same posts.
Twitter highlighted the fact that their @reply feature did not work in the manner that they wanted, and will likely bring the concept back in a new feature. This is important for Twitter as they need a discovery feature of some sort because Facebook and FriendFeed do have this in some manner. Facebook has a sidebar widget of “people you may know” based on your demographic information and your current network of friends. The widget works well enough, but it could be much better. FriendFeed uses the “friend of a friend” (FOAF) model, where you may see content that your friends liked and you are not currently subscribed to. This also works fairly well, but sometimes there can be a lot of noise in the FOAF data.
Blog search is another area where discovery has been woefully lacking. Technorati tried with “fans” of blogs and the tags. The tags generally work for categorization, but there is little help in discovering new blogs or content.
On the recommendation side, we have Amazon. Their recommendation feature has made them a lot of money. However, if you have ever bought gifts for people, your recommendations start to skew in odd directions. Amazon has taken steps to avoid this, like not using a purchase for recommendations, but fine tuning the recommendations requires a lot of user input.
So, what is the problem? First, real discovery is hard. Amazon has been doing this for years and still has some problems. So, newer services will likely have problems with it as well. Discovery also takes knowledge of advanced topics like statistics and machine learning. People well versed in these topics typically are not well versed in usability. The other side of the discovery problem is time. Typically, discovery takes a lot of time and CPU cycles. We are starting to see a lot of data in the social media space, which means it is ripe for mining and applying discovery. Some of the newer technologies like BigTable, Hadoop and many of the cloud related initiatives could help us along the way. I have a feeling we could be primed for a breakthrough in discovery, and I wish I knew who would provide it.