On Social Network Analysis and Recommenders

An interesting area where I drifted today is the crossing point of recommender systems and social network analysis (SNA). I read a few papers in a row about it (Rashid et al., 2005; Korfiatis et al. 2006, not published; Carcia-Barriocanal&Sicilia, 2005),

The other day I chatted up with my PhD study-buddy on SNA and it was actually quite enlightening. I was seeking to understand what is the difference between what most (old-school) recommenders do, and what does SNA have to offer to this. SNA are used to better figure out what groups do and how do they form, etc, but I lacked the understanding of how to use this for something that I want to do, i.e. enhance the discovery and re-use of LOs in a repository.

I am an avid believer and lover of social bookmarks. That's it, it's out. I think we could do so many things better just doing that. I, of course, just have to prove that in my PhD, and find a way to prove it, so it helped to talk with my buddy who is researching stuff somewhere between behavioural economics and social network theory. He had the words that I was lacking for social navigation – you get in a social space and you don't have any clues of what is out there. What do people do. They follow others, they need a guide. Say, you see other people going one way and you follow. That is what I see social bookmarks offer you, a guide to go ahead, a direction, a pointer to start. But there is also another aspect to it, bookmarks offer connections, relations between me and things I like, and then again, between things I like and other people who like the same things.

Which brings me to - how can we leverage this for information retrieval (IR). Sicilia and Garcia, 2005 and Korfiatis et al. 2006 (not published) talked about this: to bridge the areas of Social Network Analysis (SNA) and Information Retrieval. In a way, already the famous PageRank is about social networks, who endorses whom in a form of a hyperlink. The only problem being is that we do also link to things that we don't care about...but back to recommenders...

Until somewhat recently recommenders were about ratings and explicit values that people gave to items. The big deal was inferring those values for users who had not explicitly done that or even interacted with the item. Nowadays we are moving into using all other kinds of data as an input for recommenders, like the context-aware attention metadata that my colleagues are looking into.

The idea of Contextual Attention Metadata-framework is that it would log data from different application that a user is using for the e-learning purposes. The fact is, that nowadays we are getting further and further away (at least mentally) from single big Learning Management Systems (LMS) and are more and more looking into using small “comfi” tools (IM, bookmarks, wikis, blogs,..) for learning purposes too. All these tools can generate attention metadata, and a framework like CAM could track that. A step ahead from conventional data-mining from separate and sparse log-files.

So, now are are looking into contextual attention metadata that can arch across application boundaries and tell us stuff like: after watching that educational movie, the learner 3 contacted a tutor by IM and then spent an hour working on a text editor while surfing on the Web using x and y keywords. From that we can try to deduce things (like how the learner actually uses the learning tools and material) that we could use to make more personalised recommendations.

What I find more interesting, though, is the social context, like PeopleRank (Carcia-Barriocanal&Sicilia, 2005; Korfiatis, 2006 n-y-p), that could be used to compliment something like PageRank. PeopleRank would use the social ties, i.e. the links that people have expressed in a FOAF-file to compliment the “conventional” the PageRank algorithm. That's cool, all right, although, just right from the bat I feel like I prefer the Yahoo's MyRank, that also uses a FOAF-description on top of their conventional search algorithm. Moreover, I would be interested in finding some other ways to use the FOAF-file, which I'm trying to think of. Maybe some more interesting things could, in deed like suggested by Carcia-B..&co, come from the use of foaf to express relations between organisation or group (schools, educational projects,.- like we could use it in our EUN-context), instead of individuals.

Well, back to my bookmarks and tags: I'm interested in observing on what happens in a repository of LOs where users can bookmark learning resources, socially navigate them in other people's collections, when tags are used and when people can rate and evaluate LOs that they have in their collections. Furthermore, we like to facilitate the creation of lesson plans, like one would create play lists in iTunes.

Recommending educational material to teachers and learners, automatically sequencing course material or aggregating learning resources and delivering personalised learning has in many research oriented projects relied on pedagogical concepts, on learners learning styles, on assessment of previous knowledge and skills, etc. This is probably very useful and has undoubtedly many potentials. (First we only need kind of standardised testing to assess skills and then plentiful pool of varied learning resources that comply to any different learning style, oh yeah, and which definition of learning styles are we going to use...).

Instead, I'm interested in tapping into the social power of a group of educators and their knowledge about what learning resources to use and in what case. Instead of looking into personalisation-side of things, I want to see what happens if we just look into socialisation-side of things. Do like others have done-kinda idea. If other people cross the street here, maybe I should cross it here too.

Of course we would have to assume that there are some personalisation going on, each case is unique, after all. But still many cases do resemble one another. And maybe looking into social navigation in an educational context can help us to unlock the problematic and labour intensive questions of recommending educational material.

Additionally, bookmarking comes with tagging, user-generated keywords that people can assign for material to find it later. That's the personal knowledge management side of things. Tags can also create communities, people interested in same things eventually end up using similar names/tags and thus a link is formed. Tags also make us understand better the different meanings and ways that people can understand “a thing”, etc..In the LOR-context tags could make explicit some of the teachers' "folk pedagogy" type of knowledge. Folk pedagogy can be an accumulated set of beliefs, conceptions and assumptions that professors personally hold about the practice of teaching (Bruner, 1996). Maybe this can also unlock something that we don't know of, yet.

Well, some thoughts that I've decided to write down to keep track of my thought.