- What did you find interesting about the article?
I like the article in its totality since I worked on developing a web-based filtering system for search engine results while at Lucent Technologies. Later, while at AT&T, I had many opportunities to talk with Steve Whittaker, one of the reviewers of this article, and discuss some of the work he was doing on priority-based filtering/organization for email. The idea of community-based filtering for the betterment of the community itself is fascinating!
- What did you find not interesting about the article?
As usual, I have a question: In their discussion of weblogs, I believe the authors did not address the issue of bias in recommendations when the weblogs are populated with annotations from a single or a few individual(s). Could you please go over this problem in class and how bias can be computationally reduced or eliminated (other than resorting to the obvious option of increase the number of recommenders).
- What do you consider the main message of the article?
The main message of the article is that people are faced with endless choices when searching for information on the Internet and the WWW, and in order to make thier lives easier, Recommender Systems evolved so that "people could share their opinions and benefit from each other's experience". By implicitly or explicitly collecting corpus, these systems enable people to help each other in making informed decisions.
- Describe how you rely on non-computational recommendation systems in your daily life?
For things like movies, I look up reviews online at IMDB BUT before I actually go to watch a movie, I ask some of my friends if they have seen the movie and what they think about it. I have different categories of "movie watching friends" (commercial, independent, folk, etc.) that I go to selectively based on the genre of the movie I am interested in watching.
Also, while deciding between graduate schools, I emailed/talked-to many of my friends, current students at those schools, and professionals so that I could get a more "personal" recommendation other than the regular NRA rankings on the WWW.
- Have you used any of the systems mentioned in the article (e.g., slashdot)? What motivates you to use them?
Yes, I have used Slashdot, CDNow, Amazon.com, and some other systems that were mentioned in the article. As indicated in the article, I too have to rely on the recommendations posted by other users to decide between scores of options that are presented to me when I conduct a query on the WWW.
- Choose one of the systems mentioned (see URLs in the article and in the slides for the lecture) and briefly describe your experience with the system?
I am a recommender as well as a seeker for Amazon.com system. I initially acted only as a user since I was not aware of the "level of expertise" required to become a recommender. After having read and successfully used recommendations about books from other "regular" buyers, I felt the need to input my thoughts on books that I had read; a responsibility to "give back" atleast some percentage of what I was getting out of the system.
