Enabling Serendipitous News Discovery Experiences by Designing for Navigable Surprise

We formulate a user-centric approach to promoting serendipity in news recommender systems that leverages user familiarity with the algorithmic language of recent social media (in particular, TikTok) to nurture news discovery. We conceptualise serendipity in recommender design as the capacity of the system to produce 'navigable surprise', defined as the experience of encountering novel, diverse, relevant and unexpected information under conditions of immediate (i.e, real time) and bounded (i.e., item-oriented) agency. This conceptualisation builds upon the notion of 'reliable surprise', to explicitly incorporate the temporal agency available to users in their repeated interactions with the system. This agency allows users to constrain the degree of “anarchy and chaos” when encountering novel and unexpected information, affording them the capability to “expect the unexpected” by engaging in the groundwork and observation required to perceive an encounter as serendipitous. To realise navigable surprise within news recommender design and situation, we propose a combination of short-term interest modelling with consumption-based (implicit) user signalling. As such, we consider the centrality of short-term interest modelling to serendipity in recommender design; concerns that have conventionally been addressed separately within the literature.