I thought the idea of creating algorithms for computing recommendations was quite interesting. I wonder how far one could really go developing algorithms for computing recommendations. Through evaluating statistics about certain information one could go very far with algorithms that compute recomendations
What did you find not interesting about the article?
Nothing imparticular
What do you consider the main message of the article?
I thought the entire article was really a review and discussion of the types and forms of recommendation systems.
Describe how you rely on non-computational recommendation systems in your daily life?
Everyday I listen to what peers have to say about different things, i.e. new movies, the latest music, or latest technologies. This is one way in which I rely on non-computational recommendation systems.
Have you used any of the systems mentioned in the article (e.g.,
slashdot)? and what motivates you to use it?
I use google everyday, because I find it is one of the best search engines.
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 find that google always returns good hits for information related to programming. Sometimes I struggle with typing the right combination of words to get what i am looking for so I get less valid hits than I would expect. Although for the most part when I query for something, Google is right on with a solution or suggestion.