On March 14th, I presented a paper about the SIRUP project at IUI’17. IUI stays for Intelligent User Interface, and it is an international conference where the Human-Computer Interaction (HCI) community meets the Artificial Intelligence (AI) community. It is a highly competitive venue, with an acceptance rate below 25%. Our paper introduces a model for serendipity in recommender systems using curiosity theory. Here the abstract of the paper:
In this paper, we propose a model to operationalise serendipity in content-based recommender systems. The model, called SIRUP, is inspired by the Silvia’s curiosity theory, based on the fundamental theory of Berlyne, aims at (1) measuring the novelty of an item with respect to the user profile, and (2) assessing whether the user is able to manage such level of novelty (coping potential). The novelty of items is calculated with cosine similarities between items, using Linked Open Data paths. The coping potential of users is estimated by measuring the diversity of the items in the user profile. We deployed and evaluated the SIRUP model in a use case with TV recommender using BBC programs dataset. Results show that the SIRUP model allows us to identify serendipitous recommendations, and, at the same time, to have 71% precision.
The paper is available here.