Further to my posting on Shirky's Semantic Web article, here are a couple more interesting postings:
And via Blackbelt Jones, a cool rejoinder to anti-semantic arguments from Paul Ford: Harper's Magazine.
Via Tom's theOTHERblog, a very interesting link to Patterns in Unstructured Data
Discovery, Aggregation, and Visualization. Well worth a read.
Via iaslash, here are a couple of articles by Stowe Boyd on social networks: The Promise and Pitfalls of Social Networking and Cracking the Social Code. I especially appreciate this opinion in the light of my previous thoughts:
"It is perhaps true that the enemy of my enemy is my friend, but it is not clear that the friend of my friend is a friend of mine. Trust and regard attenuates rapidly in human relationships: a friend of a friend of a friend is unlikely to bring much social juice to bear. Except in narrowly defined and strongly affiliated groups (like religious sects, fraternities or within corporations), this transitivity of social capital does not really work."I think I'll have to think this through in a more socio-mathematical way - what aspects of online trust that are of value can be tracked and transmitted and how can these be usefullly exploited?
I've already ranted about this multiple times. Here's a back up quote via an interesting article called Design by or for the people? (via Infodesign) from Robert Brunner:
“it really doesn’t matter if something is usable. What matters is that it is in fact, useful. And even better if it is desirable.". Let's make stuff useful - usability is a part, not the whole of this equation.
Was reading Knows and Memes from Many to Many - reminded me of the piece I was writing a while ago called Weblogs: dynamics and value. Strikes me that the transmission of memes "themes" could usefully expand some of my thoughts...
Maybe this is the sort of hook that trust-based search could leverage: Microsoft is apparently developing wallop, which leverages Instant Messaging groups and contacts for "social software" purposes. Not much detail available yet.
Read the following:
Peter M's opinions about epinions have sparked my interest, I must check it out a bit.
In entirely solipsistic and self-absorbed fashion, here are links to photos of me, my beautiful girlfriend Severine (to whom I'm getting married in a fortnight!) and Eric who came to pay us a visit in Glasgow a couple of weeks back
I was reading Clay Shirky's The Semantic Web, Syllogism and Worldview, and found it a very interesting and challenging article. I do have a couple of thoughts on his ideas, however, and am not in total agreement.
Anyway, I'd recommend the scepticism of the article: healthy, intelligent stuff. On another note, I noticed the following with interest toward the article's close:
"Social networking services [...] assume that people will treat links to one another as external signals of deep association, so that the social mesh as represented by the software will be an accurate model of the real world [...] and as a result, links between people on Friendster have been drained of much of their intended meaning. Trying to express implicit and fuzzy relationships in ways that are explicit and sharp doesn't clarify the meaning, it destroys it."my italics
Here are some comments from others more worthy of commenting than myself (care of Blogdex):
The paper from Macedo et al isn’t a million miles away from my idea. Essentially, it appears that their WebMemex system (apologies to Mr V. Bush? He’s in the references!) is made up of a tool to track a users web activity (ie. a record of page requests), a tool to link documents together by commonality of terminology (using Latent Semantic Indexing theory), and a system to recommend documents via the web activity of other people in an identified group.
There are a few things I found interesting and useful in this approach (other than Latent Semantic Indexing, which I read about, but didn’t understand, in another paper on a different issue):
I really like the fact that the social network is established outside of the application itself. The ties of trust should be able to work for other non-overlapping applications (in the same way that Sainsbury’s, BP, Barclaycard and Debenhams and share the Nectar Loyalty card): say for instance that Google, Amazon and Lastminute all used the same outsourced “trust” recommender system. This would reduce the burden on the user of duplicating information (single sign-on and then opting in to which companies can use your information), make it easier to maintain and much more likely that people would sign up to it (I reckon).
Equally, I appreciate the touch of protecting privacy through the use of a trust network only being valuable with anonymised input. I trust my friends’ opinions, but I don’t necessarily want to know which friend has made the recommendation (online trust being different in many ways to face-to-face trust).
It’s also made me think a little bit more about the trust network: where does the value come into play? I don’t really agree that reciprocation is necessary for trust – it’s more a case of mutual trust, so I don’t think it should affect any “trust values”. Additionally, I feel that mutual trust networks are more likely to be closed: closed networks in being less expansionist are less likely to be of use in covering a large dataset. I think that stronger and weaker trust links may overcome this: if I trust person A who in turn trusts person B, I may have some implicit trust in person B’s opinion, albeit at a lower level than that which I have with person A. As a rule of thumb, I would have thought that the small world six degrees of separation could express the outer boundary of trust value.
On another point, the principle of “it can be assumed that friends have common understandings and interests” expressed in the paper is an important one for trust. We cannot be 100% sure that I always trust A’s opinion on every matter, but it will be too time-consuming a task to filter out the unnecessary information. However, if the above assumption is true, value should come from the fact that A makes his recommendations in generally similar spheres to your interests, and that you are likely to be searching for recommendations within these spheres – which is the whole principle behind the pattern-matching side of recommendation engines anyway.
Overall, I have returned to my original idea, that, rather than seek to augment the quality of overall search results with a “trust weighting”, which I have been informed may be mathematically muddled in principle, a “search trusted sources” advanced option is the best bet. Essentially this could mean that a search engine could use keyword matching and popularity for a general trawl search where you’re just looking for as much information as you can find on a particular subject, and offer a “trusted sources” search where you really need to believe in the quality of the found artefacts (and then other searches for particular, known items etc.)
Anyway, this all bears a lot more structured thought than 15 minutes bashing it all out on a keyboard – I might return to this subject later. All comments appreciated, particularly those that may point out the errors in my logic!
I haven't had a chance to read the full study yet, but the following from Automatically sharing web experiences through a hyperdocument recommender system
sounds very close to my idea:
"As an approach that applies not only to support user navigation on the Web, recommender systems have been built to assist and augment the natural social process of asking for recommendations from other people. In a typical recommender system, people provide suggestions as inputs, which the system aggregates and directs to appropriate recipients. In some cases, the primary computation is in the aggregation; in others, the value of the system lies in its ability to make good matches between the recommenders and those seeking recommendations.In this paper, we discuss the architectural and design features of WebMemex, a system that (a) provides recommended information based on the captured history of navigation from a list of people well-known to the users --- including the users themselves, (b) allows users to have access from any networked machine, (c) demands user authentication to access the repository of recommendations and (d) allows users to specify when the capture of their history should be performed. "
More once I've read it.
If it's worth saying...then someone's already said most of it in a better fashion already! Check out this detail on search from Autonomy (obviously). Although it doesn't touch on my trust issue, it does cover the pros and cons of the other bits and bobs, and talk about Bayes much more authoritatively.
I was idly leafing through a book on probabilistic theory and the Web this evening - it was more than a bit beyond my maths, to be honest - and it set me thinking a little bit about recommendation and trust. [NB: I wrote a related piece about search a while ago, trust to my mind being the missing factor (after precision - i.e. accuracy of match between search query and returned results - and recall - i.e. number of references returned related to search query, whether full or partial matches) in search.]
From what I understand - and I might be miles out here - recommendation systems work on probability and/or pattern-matching. The probability side of it appears to key in to Bayes Theorem, which I seem to remember was a British reverend's realisation that you can make accurate forecasts by starting with a proposition and then amending that proposition as you receive a greater amount of information on that proposition. In the case of a recommender system for purchases, the more purchases the user makes, the greater certainty with which the system can predict accurate recommendations.
The other approach is pattern matching, where you have a matrix of users against purchasers and you can essentially say: ah, this user A bought X item and Y item and this user B bought X item, Y item and Z item, let's recommend item Z to user A (grossly oversimplified explanation, I know). Obviously this approach can be boosted by an ontological approach where the items are also related in an ontological scheme: this is really the approach with those books where you look up the title of an item that you liked and you get a "if you like this, you'll like these other books related by genre, theme, author etc. I don't know if the recommender systems use this, but I guess they do.
(Of course there's the self-evaluation method whereby you rate items you have bought or might buy, but that's a lot of items to rate. Trust networks would let a relatively small number of people rate a broad range of items for you with less effort)
My theory is that a far greater response to the accuracy of recommender systems would again come if you could associate trust in another's purchases via these systems. In this case, user A might thus far have purchased items X and Y, but he trusts his friend C's opinion, who has bought items X, Xa and Xb. In this case, if A could initiate a "bond of trust" between himself and C, the system could give a greater rating to C's purchases. If A trusted C and D (where D had bought items Xa, and Z, the recommender would have a great deal of information to go on.
Make recommendations to A: user B has a similar purchase pattern, but is not "trusted", weight his recommendation of Z as 1. A has bought items X and Y in a series (Bayesian approach), so is likely to buy Z, Bayesian rating 0.5. A trusts C and D who have each bought Xa, rating 3. A trusts C, who bought Xb, rating 1.5. A trusts D, who bought Z, rating 1.5.
Result: Z - 3; Xa - 3; Xb - 1.5 : strongly recommend items Z and Xa, recommend Xb if it is related in the ontology or if further purchasing evidence boosts its rating!
I know weightings are not usually additive, but I think it's a nice argument. The difficulties come in in displaying and storing the trust information (and for users to identify those they trust) - plus would people "trust" a store that asked them to give up relatively important (and, to an extent, confidential information: data protection laws spring to mind).
Just a thought anyway.
Via Tom Smith, here's an interesting use of mobile photos: extracting longitude and latitude information from jpegs! I reckon that this is another use for the emergency services (see my earlier posting from July) - swift pinpointing of a road accident victim's position, or also even criminal evidence during a court case (proof that someone was in a particular location at a partiular time)...interesting.
Care of Unraveled, a little Don Norman wisdom:
- Sometimes it’s not enough to make things work well. They also have to be fun.
- Sometimes it’s more important to make things fun than to makes things work well.
- We’re selling HCI wrong. It’s shouldn’t be about usability as much as user experience. If we’re going to make a difference, we need to talk the language of business. Consequently, it doesn’t matter if you have wonderful [usable] products if you’re not making money.
I pretty much agree with most of this - although I'm not sure it's the "whole truth" with regard to UX and usability. However, I do like the second point, as we should never forget the serendipity of people using tools for a purpose for which they were not designed, but which they achieve better than any designed tool for the purpose. I reckon that to an extent usabilty could lead to an over-engineered fit-for-purpose on the odd occasion: like anything else, all this emphasis on user-focused design is a single blind alleyway. Design should take into account all parties, materials and budgets. I seem to remember Don Norman making a quote about not designing for [either] budget OR usability OR aesthetics, but for budget AND usability AND aesthetics at the same time.
Because we are nice chaps, authors in our intranet CMS can create their own "subsite" menus. My problem with this is that they often make a cack-handed job of labelling and grouping. I've written an IA introduction in the CMS how-to guide, but I'm convinced that few - if any - actually read it. How do I get some basic IA knowledge into their heads, and how much do I give them?
Nothing to do with my company, this one. Just the joy of Macromedia. I started learning Baby Perl a short while ago, got so far, and sort of let it slide. In my search for an easy introduction to dynamic site programming, I dinked into Coldfusion Express (erstwhile product of Allaire, now swallowed and re-jigged as Coldfusion MX by Macromedia). An absolute joy so far - not done much with it, but it's relatively sweet & being tag-based easy to get to grips with (so far). I'll post some more when I get more proficient with it. Maybe time to write my own blogging software?
Via a Many to Many posting, the idea that People are the problem is one that resonates particularly strongly today.
Even the most usable tool has its problematic users who just do not appear to grasp the concept even when training is reinforced by follow-up sessions, support, feedback mechanisms etc etc. I'm irritable today because a user has blamed his/her likely loss of data on our CMS. Now our CMS has its faults, and may be to blame, but experience suggests with this indvidual that it is user error. This isn't a usability issue, because users are warned at every step to save, delete etc. which I feel this chap might not have done. The thing that got my goat is that he automatically blamed the system: this despite the fact that all usability textbooks say that users blame themselves for system errors - obviously the inverse case also applies! Nil desperandum...
Watching the Open University Cooking Programme on Saturday morning (lying in bed, natch), I discovered the following fascinating fact: both the London Stock Exchange and Lloyd's shipping brokerage developed on the sites of coffee houses. Coffee houses in the 18th Century were renowned for bringing together diverse groups of people and for being places where "anything goes" conversations were accepted. I suppose this neatly ties up the anti-capitalist demonstrators' targeting of Starbucks - but then the French Revolution was fomented in coffee shops too.
