In a previous post, we already talked about including serendipity in urban mobility. Let's take this a little bit further: imagine you are going on a city trip. During this city trip you will–we hope– have an open disposition to experiencing serendipity: it does not matter whether you’ll get on your next destination on time, as long as you are having an interesting experience. This makes the possibilities when designing such trip planner endless.
Serendipitous navigation instructions
Let’s start with your interaction with your phone. When using a traditional route planner, you’ll get step by step navigation instructions that are very precise: “After 100m, turn left”. This kind of design, we hypothesize, does not take advantage of your open disposition to serendipity. To the contrary: you will be fully consumed by your phone, trying to follow every navigation instruction word for word, step by step.
Instead, in the Serendipity Engine project, we propose presenting the user with more vague navigation instructions, that do take advantage of your willingness to experience serendipity. Take for example instructions that are easy to remember so you do not need to look at your phone while traveling, or instructions that will even make you interact with people around you.
One such navigation plan with increased potential for serendipity could be:
You arrived at the train station. Take the street with the platanus trees and continue until you arrive at the water. Follow the water upstream, where you’ll get a chance to spot Trumpeter Swans, to the heart of the city.
Behind the cathedral you’ll find lunch restaurants with options for your dietary constraints. After lunch, you’ll be interested to see the exposition on typography at the Museum of Modern Art, which is a 5 minute walk from this area.
To get back home, find your way back to the train station. Walking back takes about 40 minutes. Your train back home is 10 after each hour.
Serendipitous data discovery
At the heart of such navigational plan is serendipitous data discovery. Not only do we need to raise the potential serendipity in our navigation instructions, we also need it in the algorithms that crawl the web for potential datasets that could influence the navigation. A combination of personal data with open datasets can make all the difference here: in the above example, both the fact that you are potentially interested in birds, and the fact that there is a specific kind of bird in that city, need to be discovered and used.
The Solid project makes your personal data, at least with your agreement, reusable for other applications. That way, your personal data will become a great source for personalizing your experience on the Web. With the Comunica querying agent, we are able to perform Link Traversal based querying. A query planning algorithm will need to decide what the next link is that will be followed in order to continue executing the recommender engine. Deciding that next link can lead to surprising yet pleasant encounters.
In the Serendipity Engine project we will explore just that: can serendipity be taken into account while performing a link traversal algorithm on the Web?
Does this spark your interest?
We hope to meet you along our way, serendipitously or meticulously planned. You might increase the chances of staying informed about the Serendipity Engine project by subscribing to our newsletter or connecting on LinkedIn.
Any questions in the meantime? Feel free to contact us at firstname.lastname@example.org.
This post is written by IDLab (Ghent University). IDLab is mainly responsible for the work package on Data Discoverability. IDLab's main researchers on this project are Bryan-Elliott Tam and dr. Pieter Colpaert.
This text is intended for a general audience; if you are looking for a more in-depth discussion of some of the concepts, please refer to the linked academic publications. You may also want to check out the publications of our research team.
(Cover image by Conny Schneider)