20 March 2008

Weekly Recommender Log

This week I continue my exploration of the iGoogle Recommendations Tab. And still it puzzles me.

I am told that the 6 categories of recommendations on this page - all in their own little content boxes - are updated every day, that's right, every day. This sounds strange to me. Google has built a huge company by tossing out recommendations right and left. What could they possibly be doing that takes a day? I am not all that impatient, it's just that this seems so much different than their normal business practice.

And then there is the almost total lack of recommendations. That's a little strong but I only have recommendations in one of the six boxes. And, one of the boxes continually shows an error message.

So, I am left with one of six to work with. It is the Recommended Pages box. In it I find a link and heading for 50 recommended pages, each with a opportunity to vote the link up or down. As a whole, it is a pretty lame list. I don't see how more than a handful could have been based on my search history or my iGoogle profile.

But, in the interest of forwarding the technology, I dutifully click though all 50 recommendations and give them a thumbs up or down (mostly down unless it matches some part of my online persona).

I can't wait to see what the next round brings my way. Let's hope that it won't recommend anymore weather sites for Dayton Ohio.

21 February 2008

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.

14 February 2008

I Cann't Recomment This Service - SeenThis?

http://seenthis.loomia.com/

http://www.facebook.com/apps/application.php?id=10066213622

Here we have a new facebook application that allows you to "take your social network with you".

Loomia has just released a new application - SeenThis? - that allows users on media sites (Wall Street Journal, NBC), with just a click, send their facebook network their latest recommendation. This recommendation gets combined with the other recommendations of your friends / groups / networks and presented to them while on facebook.

Loomia gets all excited about the flip side of the application. While on a partner site, say, the Wall Street Journal, you get to see what your facebook contacts think about the current articles.

It seems that you are never alone with SeenThis? and facebook.

I don't much like this whole idea. I am not interested in sharing this kind of information. I am also not interested in seeing what all of my buddies are reading. And, just when I thought I was all alone with this kind of antisocial networking bias, I looked at the second link that shows that not very many people are using this service. Somehow this makes me feel better about life on our planet.

Weekly Recomender Log

Last week I reported that I had moved to Charleston, SC. I am sad to report that Google doesn't believe me. I logged on to my Google account and noted that the logo was sporting a Valentine's Day theme. So, I thought that I would do some shopping. I searched and clicked away on all sorts of romantic ads.

Then I noticed that I was getting some ads for romantic getaways in Charlotte & Asheville - something from my past must be at play.

I found a couple of additional places in Google to plug in my zip. And after a few more shopping sessions, I no longer was invited to North Carolina locations. Apparently, South Carolina doesn't have have any romantic spots - at least not ones that need to advertise.

I found something that I hadn't seen before on Google - a gadget from Google called "Interesting Things For You". It seems that they can keep an eye on my activities and find things that I would find, well, "interesting". All of this must take some time because the tool currently reports:

There aren't enough searches for interesting items yet. Check back after searching for a couple days and be sure you're signed in when you search.

I will try harder.

07 February 2008

Weekly Recomender Log

This week I moved to Charleston, SC - at least my Google persona is in Charleston! I searched the real estate market and settled into the James Island neighborhood. My new zip code is 29412. I am beginning to feel right at home.

This is a very strange process for me. Over the last several years, I have been concerned about privacy and have only given out demographic data if I had a very good reason. Now I am clicking like mad!

Another strange notion for me is all of the ads I am seeing. I tend to bock ads. But not now, everybody in Charleston want to sell be something. I must be important, everybody want to get to know me.

I noticed that when I search on "James Island, SC" that I got a lot of adds from Realtors that were promoting them selfs as James Island but when I click, I was taken to their generic Charleston site where I was invited to search again for James Island.

The BellKor solution to the Netflix Prize

http://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf

Here's the tell all story of how the leaders in the NetFlix Prize did it - A step by step guide to all the 107 steps to glory. Glory in this context is defined as a RMSE score of 0.8712.

I don't begin to understand more than one underlying point of this document. And, that point is that none of their individual optimizations worked near as well as a blending of the methods.

These people don't mind telling you how they do their work. A background document can be found at:

ftp://ftp.computer.org/press/outgoing/proceedings/icdm07/Data/3018a043.pdf