Week 1 Readings
FOA 1.1
I have not heard the term 'finding out about' before. I like it! This seems like a personal knowledge management system. Framing information retrieval in this way, makes a compelling argument for the usefulness of IR. Without IR, we will be less well equipped to guarantee that we are prepared to make decisions. The IR process as a dialog is a very important concept to keep in mind; without knowing that the user is satisfied, your search engine has not filled their need.
IES 1.1-1.2
In their definition of IR, it can only exist for something in a human language. I don't know if I agree with this. If the user knows how to create a useful query in a non-human language, and can process the results as relevant or not, this is IR. A Google image search using a photo as the query is an example of this that even I have done successfully.
I appreciate the discussion of desktop file search systems and enterprise IR. These are applications that are equally useful to a web search engine professionally, and from my limited experience, have much room for improvement.
The Probabilistic Ranking Principle is important to consider. It seems likely that this is always true, and that it is indeed the best way to sort results, but I would like to read the findings of anyone who has challenged this precept. I know that, for example, the fifth result was most clicked in one study, and others have found that the second page of Google results are not statistically less significantly relevant than the first page. Maybe the PRP of these pages is only very slightly different.
MIR 1.1-1.4
The IR problem is to retrieve all the relevant documents while returning as few non-relevant documents as possible. It is interesting that the + is all, but the - is not zero. Why is the goal not to return zero non-relevant documents? Is this due to partial relevance or ideas of changing relevance as the user reformulates the query?
There are many problems in IR currently, including spam, legality, size, speed, and efficiency.
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