ReadWriteWeb has an interesting post on “collaborative filtering” on social news sites. The writer makes the point that there are two types of social news recommendation models: one that provides you with the most popular stories from all members of the site, and another that provides you with stories that are personalized to you based on your past reading habits and those with similar interests as you.
There are different camps here: Digg based on the former model of popularity and others such as Reddit, Stumbleupon and Searchles that recommend stories based on various forms of personalization. (There are also services such as Aggregate Knowledge and Loomia, which use some aspects of social recommendations to recommend related articles or products.) However, the two methods, popularity and personlization, are not mutually exclusive. Digg is about to implement a recommendation feature. And many sites, from big media sites to social media sites, could provide both a most popular section as well as personalized recommendations.
The distinction between popularity and personalization is crucial. A lot of claimed personalization is really “most popular” (e.g. people who viewed this also viewed) and all visitors get the same list.
Only if different visitors get different recommendations for the same item is it personalization. And few technologies offer this.
While I agree with Paul that the two techniques are different I think that it is like comparing screwdrivers to hammers. They’re both good tools that you have to know when to apply in different situations.
The reality is that a combination of approaches depending on the data you have available about the person and the context that they are in will give the best answer.
Paul, Agreed. Some companies such as Searchles say that they are doing just that–providing a list of different people who have similar interests as you, based on your history of articles read and comments you’ve made, and then matching that to other users’ articles and comments. They are not matching based on the particular article you’re reading at the time but based on the people.
This is an important distinction.
Until 2008, most social news sites were focused on “we”
Now, especially as the volume has increased on the “we” sites, a lot of attention is turning to “me”
At socialmedian we are laser focused on solving the “me” problem — enabling every user to get a uniquely personalized social news experience based on his/her unique interests, influenced by people who share his/her interests.