Transition from Youtube Journaling to WikiLens

It appears that YouTube is not a personalized recommendation system.  Instead YouTube is only keeping track of Favorites so the user can go back and re-watch the videos again.  The ratings are not being used to recommend videos with similarities and that are enjoyed by users with similar interests in videos.  The ratings are only being used to rank how popular a video is.  Popularity determines rank in search results.  If two users with dissimilar interests that have rated videos query the same thing, they will receive the same videos.  Those videos will always link to the same videos regardless of declared preference.  The linking is determined by the user that has submitted the video and by videos with similar words in the title.

Since this is the case, I will be switching the focus of my journal to WikiLens.  It is a recommender engine that I have covered in one of my earlier posts.  I will begin establishing a more in depth user profile and will also begin a new recommender topic using the engine.  I think that it will most likely have something to do with cycling.  At the moment I am testing the feasibility of routes, bikes, races, teams, shops and vehicles for cyclists.

Recommender system and method for generating implicit ratings based on user interactions with handheld devices

This recommends items based on how a user interacts with a handheld device.  Users are given a repetitive task to do on a mobile device.  Their interactions are recorded in a history.  This history is used to create recommendations.  Unlike Amazon's content based system, this system takes into account how recent an event or interaction occurred.  More recent interactions are considered to be more relevant.

According to the claims, the calculations for the recommendations are made in the following three ways:

• "rating(item)=number of interactions(item) since datetime(item acquired)/number of total interactions (item) since datetime(item acquired)."
• "rating(item)=total interaction time(item)/size(item)"
  

• rating(item)=[total interaction time(item)/size(item)*exp(−damping coefficient]*(date−time acquired).

The first method takes into account the recency of the interaction.  The second method takes into account the amount of time that the user spent on a particular interaction.  The third method takes into account something that I don't understand.  I can't figure out what the difference is between claim 2 and claim 3.  They appear to be the same with the exception of the calculations.

  Mobile devices that the patent covers in its data gathering include cell phones, mp3 players, PDAs, and electronic book readers.

**WARNING this was probably written by a lawyer***

http://www.freepatentsonline.com/6947922.html

http://www.google.com/patents?id=QkEWAAAAEBAJ&dq=09/596070

Photoree

http://www.photoree.com/photos/tour

 The site describes itself as being "to photos what Last.fm is to music and StumbleUpon is to websites."  It is an image recommendation system that uses a thumbs up or thumbs down method of rating.  Preferences are recorded and used on the fly to recommend other images that the user might prefer.  The recommendations are user-to-user based and might cross some privacy boundaries if not careful.  The user is allowed to see the stats on how popular an image is.  It goes on to tell the user WHO liked or disliked the image.  There are security settings that the user can opt into to decide not to share their preferences to others.

***WARNING***

There are no guarantees that the images made available for rating or suggestion will not be nudity.

***WARNING***

Youtube Doppleganger

For three weeks now I have been rating youtube videos under to different accounts.  One account prefers to watch music videos and movie trailers while the other account prefers to watch stand up comedy and magic tricks. 

For both accounts ratings were done by adding the videos to the "Favorites" list and by using a 1 to 5 star system.  

Searching for 'comedy' and 'card tricks' in each account yields mostly the same results with roughly 10% difference.  'Comedy' yields the same results for both.  

The main page, channels, and 'video' section for each always shows the same items.  

The interesting thing is that the 'Promoted Videos' are different for each always.