Sweet serendipity

I’m still thinking about our Tweetorials from a couple of weeks back. During our Wednesday tweetorial, Jeremy asked:

#msccde folks gave lots of interesting answers to this question. At one point, we started talking about recommender systems and Tracey mentioned in passing that she had a certain recommender system suggest that because she listened to the Jesus and Mary Chain, she might like The Proclaimers. She joked that this is why she doesn’t worry too much about the machines taking over. (Ben joked that the same music recommender system keeps trying to play him Bing Crosby long after Bing season.) There’s something to what both are saying: supposedly ‘personalised’ recommender systems get our preferences wrong all of the time in many, often comical, ways.

This conversation prompted me to look said music-recommender-system-that-I will-not-name and yes, to be sure, there were some fairly hilarious options presented to me. But there were also many more that were more or less accurate. I listen to lots of small indie bands, and, there it was, recommending lots of little bands that I had never heard of that, upon listening, I kind of liked.  Kind of convenient, if you ask me!  (I like other things too, of course, but this is a good example for what I’m about to say). Here’s the rub: while this is nice in some ways, it’s also rather conservative. The system looked at what I listened to in the past and served up a new menu of new options based on that. But there’s something crucial that the system doesn’t know about me. What it doesn’t know is that I’ve been rather bored of its recommendations of late. I mean, I like indie music, but cripes, I wouldn’t mind listening to something new; otherwise, it feels like being trapped in some sort of Groundhog Day. On the evening of our tweetorial, it was hard to see a way out of the godforsaken indie loop, so out of the blue, mid-tweetorial, I just emailed someone (who wouldn’t think me a total weirdo for randomly emailing them like this) and I asked what they were listening to. Answer: this new album.  For the rest of that week, I listened to this one track from it (you can listen below) more or less on a loop when I was doing the readings. It’s very beautiful and it’s great to listen to if you just want to concentrate on something for an extended period of time.

What’s interesting is that I really liked this new album even though it wasn’t something that I’d ever pick myself, and certainly not something that the recommender system would have given me as an option. And this, I think, is what I love about interacting with humans as opposed to machines: the sweet serendipity of encountering others who are different from ourselves and of discovering all of the new and interesting things that go along with that. And maybe too the path out of one’s own mind and towards the minds of others: the escape from the ‘personalised’ solipsism that I talked about here.

Block 1. Week 3. Learning: Conceptual Space

This data visualisation aims to represent how a student’s conceptual space expands in response to provocations made via the use of a range of media. It hones in on four of many aspects to this topic: (1) The question (or theme or subject) that provokes a thought (in this case, on either learning, personalisation, or data); (2) The media though which these thoughts are provoked (either via course readings, Twitter, or our course blogs);  (3) The emotion associated with my own response in each case (interested, excited, puzzled); (4) The dimension of response (small, medium, large) to what is asked or under discussion.

This visualisation could be used to demonstrate how our learning processes are not just cognitive or social. They are also charged with emotion. Influenced by Bulger (2014:4), I took a shot at defining personalisation here. But personalisation so understood makes the learning process an almost solipsistic endeavour devoid of emotional tone. Individual preferences and competences are adapted to; social and emotional dispositions are ignored. When Jeremy misquoted me I was annoyed – an appropriate emotional response to what Peet (2015) calls interpretative injustice. The learning process is laden with emotion. Personalisation fails to accommodate this. It’s missing from what Eynon (2015) calls the quantified self.

Word count: 200 words.