Point of Interest (POI) recommender systems (RSs) play a primary role in improving Location-based Social Networks’ user experience. This paper studies the potential usefulness of serendipity in POI recommendations. We first introduce a new POI RS, called Discovery, that attempts to improve the accuracy-serendipity trade-off. The proposed RS aims to recommend POIs that provide a pleasant surprise, allowing users to discover new venues known as serendipitous POIs. We then look closely at how serendipity affects the quality of POI suggestions by contrasting the outcomes of Discovery with those of three cutting-edge non-serendipitous POI RSs. We use two real-world datasets—Foursquare and Flickr—along with a variety of metrics to test our ideas. These include (i) accuracy, which checks the precision, recall, and f-measure of Top-N recommendations; and (ii) beyond-accuracy, which checks the categorical and geographical diversity, explainability, and coverage in terms of POIs. The reported experimental observations show that serendipity boosts POI recommendation accuracy and favors geographically proximate and explainable POIs. However, standard POI baselines outperform Discovery in terms of categorical diversity and coverage.

Abstract We describe an experimental implementation of a logic-based end-to-end pipeline of performing inference and giving explained answers to questions posed in natural language. The main components of the pipeline are semantic parsing, integration with large knowledge bases, automated reasoning using extended first order logic, and finally the translation of proofs back to natural language. While able to answer relatively simple questions on its own, the implementation is targeting research into building hybrid neurosymbolic systems for gaining trustworthiness and explainability. The end goal is to combine machine learning and large language models with the components of the implementation and to use the automated reasoner as an interface between natural language and external tools like database systems and scientific calculations.

Abstract Commonsense reasoning has long been considered one of the holy grails of artificial intelligence. Our goal is to develop a logic-based component for hybrid – machine learning plus logic – commonsense question answering systems. A critical feature for the component is estimating the confidence in the statements derived from knowledge bases containing uncertain contrary and supporting evidence obtained from different sources. Instead of computing exact probabilities or designing a new calculus we focus on extending the methods and algorithms used by the existing automated reasoners for full classical first-order logic. The paper presents the CONFER framework and implementation for confidence estimation of derived answers.

Session-based recommendations are based on the user's recent actions, for example, the items they have viewed during the current browsing session or the sightseeing places they have just visited. Closely related is sequence-aware recommendation, where the choice of the next item should follow from the sequence of previous actions.

Mining crowd-sourced movement trajectories is a useful tool in urban computing. Common mobility patterns of the visitors or residents of a city can be exploited in applications such as disaster management, transportation planning and ad placement. In recommendation systems, individual behaviour is of special interest. To extract the visiting behaviour of individuals, the trajectories need to be semantically annotated. We describe how hierarchical regions of interest (ROIs) can be used for semantic annotation. By combining multiple layers of smaller and larger regions we can flexibly detect both visits to dense hotspots and trajectory segments visiting larger areas, such as an old town, a park or an island. Extending the annotation beyond common hotspots captures more information about the behaviour.