posts tagged ‘recommendation systems’

Want to be retweeted? Add URLs to your tweets!

16 August 2010 | Lichan Hong

In a previous post, I described a recent study in which we found that including hashtags in a tweet enhances the retweetability of the tweet. In this post, I'll focus on another factor that might affect retweetability: the URL.

Experts’ domain knowledge improves automated recommendations

9 November 2009 | Ellen Isaacs

It’s almost eerie how well the music website Pandora recommends music based on just one example of a favorite song. It does so by relying on human experts to characterize songs based on a large and musically sophisticated set of characteristics (melody, harmony, rhythm, orchestration, etc.). This approach -- of using human expertise to develop a rich set of attributes that deeply capture the essence of an item -- could be adapted to greatly improve the recommendations currently being offered in other domains, such as news, movies, hotels and so on. In our enthusiasm to develop automated recommendation systems, Pandora reminds us of the value of incorporating the intelligence of domain experts into the process.

Augmented Reality: Increasing collective intelligence

23 July 2009 | Lawrence Lee

Search engines like Google have trained us to believe we can find the answer to any question. Now activity streams from Twitter, Facebook, and others are changing our expectations around information yet again. We now demand information in real-time that’s socially and contextually relevant. Contextual information transforms our interactions within our physical environment... This area of research is called Augmented Reality, and it spans a wide spectrum of applications...

Can a recommender choose what’s best?

7 July 2009 | Ellen Isaacs

While doing a user experience evaluation of a mobile recommendation system, I noticed how the list-based design implied that there was a single best choice, with 'goodness' decreasing in even increments. This made me wonder whether it makes sense to try to identify the one Best option when you're choosing among options that vary across many dimensions. Making choices might be easier if we assume that there is rarely one single Best.