Science for SEO

There is an awful lot more information on the actual Google blog, Danny Sullivan wrote a nice guide as well, and there's a Q&A with Google about it as well.

I've asked around and most general users don't seem to have even noticed it was there.  My mum definitely has no idea what the whole thing is about, because she doesn't want to break anything, she isn't going to press on any of the buttons.  The more savvy users right down to the programmers said they weren't bothered with it either.

I keep forgetting its there and so I haven't used it very much.  I think that we would all begin to use it if we started to see the benefit.  Sadly in order to see the benefit, you have to start using it!

Google said “It’s a new way to empower users. You can remember answers to repeat queries. It lets you add your personal touch to our algorithms” (See the Q&A doc).

I genuinely think it is indeed a tool to help you alter the results to suit your particular slant on a particular query.  I also think it's a pretty cool way to collect a huge amount of user data and also human edited results provide more information on the authority of the resource.

Remember how we look at Social Media sites like Digg and said that the voting was warped because of it being so easy to manipulate?  Well seeing this is in a "closed" environment, meaning that nobody else but you gets to see it, there is no reason to manipulate the results.  Also the issue with the weighting of each vote is also no issue because it's proper to a single user.

1-800-GOOG-411 was all about collecting phonemes to feed into a machine to make voice search possible today.  I think SearchWiki is along the same lines.

ECIR 2008 took place earlier this year and Nicholas Belkin did a keynote speech on the Grand Challenges in IR.  This always feels to me a little bit like a telling off to the IR community on what we haven't done properly or even considered as yet and where we've totally failed.  It's very important for this to happen because it sets the focus on the most important challenges we currently have in IR.

As an SEO person, this is of great importance to you because it tells you clearly where IR is heading.  The message here is loud and clear: Users.

1 - There needs to be more work done in the area of specification of tasks, intentions and information behaviours, in order to go beyond straightforward listings.

2 - There needs to be more research in user-behaviour analysis for IR.  Methods can be developed to infer information related goals, tasks and intentions from previous or concurrent behaviour.  Right now all goals have to be specified. 

3 - We need to develop IR techniques that respond to the above 2 challenges (identification of goals, tasks and characterizations).  This will require an interdisciplinary approach, as there is need for HCI, IR, AI and other people to achieve this goal.  That is also an obstacle right now.

People have a hard time specifying what would help them find the information they're looking for.  They change search behaviour several times during a single session.  We don't know enough about the different information behaviours and why people engage in one behaviour or another.  

We need to identify aspects of context, identify some subset of all possible factors leading to an information seeking situation, so that we can build contextually responsive IR systems.  I'll blog about this next because it's very interesting, but basically an experiment showed that unfortunately, everything is context.  This means that we have no real way of finding such a subset, but maybe we can identify some main aspects of this to improve support for information behaviours.

So far most mainstream IR research has been all about the efficiency and effectiveness of the IR system or the performance of the user.  Affective-computing is still in its infancy, and other fields of computing are also quite behind here, but we need to acknowledge the significance of this when we look at the user's experience of the IR system.  It's all about the role of emotions in the information seeking process.  This could help us understand what subsequent actions the user is likely to take for example, and of course understand where negative feelings arise and allow us to reduce them.

There hasn't been enough work in this area yet, it's all been too restricted.  We've been looking at click through paths, time spent on a page, previous and subsequent queries, relevance feedback,...Belkin says it's not good enough because we're only using one type of evidence in isolation.  There is research being carried out which shows that everything has an effect on everything else, and so if we look at our results in isolation, the results can be misleading.

So: "The challenge with respect to personalization is first to consider the dimensions along which personalization could and should take place; then to identify the factors or values in each dimension that would inform personalization on that dimension; then to investigate how the different factors or types of evidence interact with one another; and finally, to devise techniques which take account of these results in order to lead to a really personalized experience. 

Interacting with information is usually a consequence of someone wanting to achieve another goal or accomplish another task.  We need to ensure that a person never has to interact with a separate IR system, a person never has to leave their task to satisfy an information need.  We need to collaborate with the application communities so that IR gets integrated into real task environments.

Evaluation paradigms for interactive IR

TREC is the usual collection of documents used to evaluate an IR system, but it isn't very good.  There have been efforts at collecting better data sets, but generally, this is always a big problem for the research community.  

   

New less formal models of IR, such as language modelling for example are being introduced in the research community.  It's all good work, but it suffers from the fact that they focus on issues of representations of information objects, static queries, matching and ranking techniques.  They don't focus on  the user and on interaction, so there needs to be more research into formal models of IT that are truly interactive.

...back to work then...

They say that they want to use it to discover more about how people search, share and edit information, and how they organise their search results.  You can move around your search results, delete stuff, make notes and make it all visible to your friend, and also recommending sites to them.  I like the idea of sharing my search results with people, because I often do a search for someone and then send them the best results for their information need, so this would just make it much easier.   They also offer the possibility of mixing up photos or images with video footage results, and I can see that happening quite easily.

Read Write Web have a good post about U Rank and notice that you can't move results from the second page to the first page - I think this is a pretty big problem.  The dragging and dropping doesn't work so well either they noticed.

You have to have a LIVE account to use it.