Archive for the ‘Serendipity’ Category

A long and winding road (….introducing serendipity into music recommendation)

Wednesday, April 25th, 2012

Auralist: introducing serendipity into music recommendation

Abstract:

Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised collection of recommendations that balance between the desired goals of accuracy, diversity, novelty and serendipity. We introduce the Auralist recommendation framework, a system that – in contrast to previous work – attempts to balance and improve all four factors simultaneously. Using a collection of novel algorithms inspired by principles of “serendipitous discovery”, we demonstrate a method of successfully injecting serendipity, novelty and diversity into recommendations whilst limiting the impact on accuracy. We evaluate Auralist quantitatively over a broad set of metrics and, with a user study on music recommendation, show that Auralist‘s emphasis on serendipity indeed improves user satisfaction.

A deeply interesting article for anyone interested in recommendation systems and the improvement thereof.

It is research that should go forward but among my concerns about the article:

1) I am not convinced of the definition of “serendipity:”

Serendipity represents the “unusualness” or “surprise” of recommendations. Unlike novelty, serendipity encompasses the semantic content of items, and can be imagined as the distance between recommended items and their expected contents. A recommendation of John Lennon to listeners of The Beatles may well be accurate and novel, but hardly constitutes an original or surprising recommendation. A serendipitous system will challenge users to expand their tastes and hopefully provide more interesting recommendations, qualities that can help improve recommendation satisfaction [23]

Or perhaps I am “hearing” it in the context of discovery. Such as searching for Smokestack Lighting and not finding the Yardbirds but Howling Wolf as the performer. Serendipity in that sense not having any sense of “challenge.”

2) A survey of 21 participants, mostly students, is better than experimenters asking each other for feedback but only just. The social sciences department should be able to advise on test protocols and procedures.

3) There was no showing that “user satisfaction,” the item to be measured, is the same thing as “serendipity.” I am not entirely sure that other than by example, “serendipity” can even be discussed, let alone measured.

Take my Howling Wolf example. How close or far away is the “serendipity” there versus an instance of “serendipity” as offered by Auralist? Unless and until we can establish a metric, at least a loose one, it is hard to say which one has more “serendipity.”

No Datum is an Island of Serendip

Wednesday, November 30th, 2011

No Datum is an Island of Serendip by Jim Harris.

From the post:

Continuing a series of blog posts inspired by the highly recommended book Where Good Ideas Come From by Steven Johnson, in this blog post I want to discuss the important role that serendipity plays in data — and, by extension, business success.

Let’s start with a brief etymology lesson. The origin of the word serendipity, which is commonly defined as a “happy accident” or “pleasant surprise” can be traced to the Persian fairy tale The Three Princes of Serendip, whose heroes were always making discoveries of things they were not in quest of either by accident or by sagacity (i.e., the ability to link together apparently innocuous facts to come to a valuable conclusion). Serendip was an old name for the island nation now known as Sri Lanka.

“Serendipity,” Johnson explained, “is not just about embracing random encounters for the sheer exhilaration of it. Serendipity is built out of happy accidents, to be sure, but what makes them happy is the fact that the discovery you’ve made is meaningful to you. It completes a hunch, or opens up a door in the adjacent possible that you had overlooked. Serendipitous discoveries often involve exchanges across traditional disciplines. Serendipity needs unlikely collisions and discoveries, but it also needs something to anchor those discoveries. The challenge, of course, is how to create environments that foster these serendipitous connections.”

I don’t disagree about the importance of serendipity but I do wonder about the degree to which we can plan or even facilitate it. At least in terms of software/interfaces, etc.

Remember Malcolm Gladwell and The Tipping Point? Its a great read but there is on difficulty that I don’t think Malcolm dwells on enough. It is one thing to pick out tipping points (or alleged ones) in retrospect. It is quite another to pick out a tipping point before it occurs and to plan to take advantage of it. There are any number of rationalist explanations for various successes, but that are all after the fact constructs that serve particular purposes.

I do think we can make serendipity more likely by exposing people to a variety of information that makes the realization of connections between information more likely. That isn’t to say that serendipity will happen, just that we can create circumstances for people that will make the conditions ripe for it.

Serendipity Is Not An Intent

Tuesday, November 15th, 2011

Serendipity Is Not An Intent

From the post:

Wired had two amazing pieces on online advertising yesterday and while Felix Salmon’s piece The Future of Online Advertising could be Yieldbot’s manifesto it is the piece Can ‘Serendipity’ Be a Business Model? that deals more directly with our favorite topic, intent.

…..

Twitter is the greatest discovery engine ever created on the web. But discovery can be and not be serendipitous. Sometimes,, as Dorsey alludes to, you discover things you had no idea existed but much more often you discover things after you have intent around what you want to discover. This is an important differentiation for Twitter to consider. It’s important because it’s a different algorithm.

Discovery intent is not an algo about “how do we introduce you to something that would otherwise be difficult for you to find, but something that you probably have a deep interest in?” There is no “introduce” and “probably” in the discovery intent algo. Most importantly, there is no “we.” It’s an algo about “how do you discover what you’re interested in.”

Discovering more about what you’re interested in has always been Twitter’s greatest strength. It leverages both user-defined inputs and the rich content streams where context and realtime matching can occur. Just like Search.

If Twitter wants to build a discovery system for advertising it should look like this. (emphasis added)

Inverts the advertising and when you think about it, the search algorithm. Rather than discovering, poorly, what interests the user or answer as question, enable the user to discover (a pull model) what interests them.

Completely different way of thinking about advertising and search.

Priesthood of the user? Worked (depending on who you ask) a long time ago.

Maybe, just maybe, a service architecture based on that as a goal, could disrupt the current “I know better than you” push models for search and advertising.

Is Precision the Enemy of Serendipity?

Wednesday, September 28th, 2011

I was reading claims of increased precision by software X the other day. I probably have mentioned this before (and it wasn’t original, then or now) that precision seems to me to be the enemy of serendipity.

For example, when I was an undergraduate, the library would display all the recent issues of journals on long angled shelves. So it was possible to walk along looking at the new issues in a variety of areas with ease. As a political science major I could have gone directly to journals on political science. But I would have missed the Review of Metaphysics and/or the Journal of the History of Ideas, both of which are rich sources of ideas relevant to topic maps (and information systems more generally).

But precision about the information available, a departmental page that links only to electronic versions of journals relevant to the “discipline,” reduces the opportunity to perhaps recognize relevant literature outside the confines of a discipline.

True, I still browse a lot, otherwise I would not notice titles like: k-means Approach to the Karhunen-Loéve Transform (aka PCA – Principal Component Analysis). I knew that k-means was a form of clustering that could help with gathering members of collective topics together but quite honestly did not recognize Karhunen-Loéve Transform. I know it as either PCA – Principal Component Analysis, which I inserted in my blog title to help others recognize the technique.

Of course the problem is that sometimes I really want precision, perhaps I am rushed to finish a job or need to find a reference for a standard, etc. In those cases I don’t have time to wade through a lot of search results and appreciate whatever (little) precision I can wring out of a search engine.

Whether I want more precision or more serendipity varies on a day to day basis for me. How about you?