Weekly Recomender Log

I start the this week exploring the world through the eyes of Google.

Last week I added a new Interesting Items For You gadget to my Humor tab of iGoogle. When I logged in this week, I still didn't have anything recommended but it suggested that I click to setup a new tab just for recommendations - just for me. I felt honored, so I clicked.

I now am the proud owner of a new iGoogle tab called Recommendations. It plans on finding items of interest for me in the following areas:

• Videos
• Pages
• News
• Groups
• Gadgets
  
• Searches

So far, only the "Pages" box has anything in it. It shows me part of a political news blog and asks if I like the recommendation - Yes or No. Sadly, I vote no. I note a link that brings up another snippet, again I vote no. I do this two more times and decide that I better do some searches if I am ever going to see more than fairly random stuff.

I start off the session with only 37 searches and the goal of seeing when the recommender will kick in. I start searching for skin care information. I click a few adds. I refine my search using Google's recommended searches. I Click an ad that takes me to Science Daily's skin care news, which happens to feature several Google ads prominently displayed in the middle of news stories. I click on one of these ads a learn about the science of skin care.

When I make it back to Google and, once again, search for skin care, I find that the top site on the non-advertisement list is my old friend Science Daily - kind of curious behavior.

I went on to search for an elusive vacuum sweeper filter - not available in stores.

I ended the day with 50 searches but still no new recommendations on my Recommendations tab. Then I noticed that the text in the boxes said: "You have no recommended searches for today."

It looks like gratification must be deferred. They update these recommendations daily.

Personalized Conversational Case-Based Recommendation

http://www.cs.utah.edu/~cindi/papers/ewcbr.pdf

This is a foundation document that the authors (Goker & Thompson) published back in 2000 and has been referenced by them, and others, multiple times over the years.

The team built a recommender system that featured a conversational approach to a personalized recommendation - in this early case, a restaurant choice. The system was know as the Adaptive Place Advisor.

This research was funded by an automotive company, so we shouldn't be surprised that the target user is busy driving a car. The interactive user interface uses voice prompts and voice responses. The system gets to know the user over time and has the goal of not only providing agreeable restaurant choices but an improved user experience over time.

An examples of how the system adapts to the user: The system will delay the repeated recommendation of the same restaurant, even if the user likes the recommendation - the thought being that even if you like cheap Mexican food, you probably don't want to hear about Poncho's every day.

It's a good reference that addresses many of the issues of building this kind of recommender.