Explainable Recommendation Systems (RS) enhance the user experience on online platforms by recommending personalized content, as well as explanations for the given recommendations to add transparency and build up trust in the platforms. Extending the notion of explainable RS, in this paper we define Why-Not explanations for recommendations that were expected but not returned, and propose and implement a technique for computing Why-Not explanations in a post-hoc manner for a graph-based RS. Our approach builds on the notion of counterfactual explanations in the means of a set of user-rooted edges to add or remove in the graph, in order to place the missing recommendation to the top of the recommendation list, and provides in this way actionable insights on the source data and their interrelations. Our experimental evaluation on a real-world data set demonstrates the feasibility of our proposal and reveals interesting directions for future work.

Several application domains, including healthcare, smart building, and traffic monitoring, require the continuous publishing of data, also known as time series. In many cases, time series are geotagged data containing sensitive personal details, and thus their processing entails privacy concerns. Several definitions have been proposed that allow for privacy preservation while processing and publishing such data, with differential privacy being the most prominent one. Most existing differential privacy schemes protect either a single timestamp (event-level), or all the data per user (user-level), or per window (w-event-level) in the time series, considering however all timestamps as equally significant. In this work, we define a novel configurable privacy notion, landmark privacy, which differentiates events into significant (landmarks) and regular, achieving to provide better data utility while preserving adequately the privacy of each event. We propose three schemes that guarantee landmark privacy, and design an appropriate dummy landmark selection module to better protect the actual temporal position of the landmarks. Finally, we provide a thorough experimental study where (i) we study the behavior of our framework on real and synthetic data, with and without temporal correlation, and (ii) demonstrate that landmark privacy achieves generally better data utility in the presence of landmarks than user-level privacy.

In data streaming, why-provenance can explain why a given outcome is observed but offers no help in understanding why an expected outcome is missing. Explaining missing answers has been addressed in DBMSs, but these solutions are not directly applicable to the streaming setting, because of the extra challenges posed by limited storage and by the unbounded nature of data streams. With our framework, Erebus , we tackle the unaddressed challenges behind explaining missing answers in streaming applications. Erebus allows users to define expectations about the results of a query, verifying at runtime if such expectations hold, and also providing explanations when expected and observed outcomes diverge (missing answers). To the best of our knowledge, Erebus is the first such solution in data streaming. Our thorough evaluation on real data shows that Erebus can explain the (missing) answers with small overheads, both in low- and higher-end devices, even when large portions of the processed data are part of such explanations.

Answering a Why-Not question consists in explaining why a query result does not contain some expected data, called missing answers. This paper focuses on processing Why-Not questions in a query-based approach that identifies the culprit query components. Our first contribution is a general definition of a Why-Not explanation by means of a polynomial. Intuitively, the polynomial provides all possible explanations to explore in order to recover the missing answers, together with an estimation of the number of recoverable answers. Moreover, this formalism allows us to represent Why-Not explanations in a unified way for extended relational models with probabilistic or bag semantics. We further present an algorithm to efficiently compute the polynomial for a given Why-Not question. An experimental evaluation demonstrates the practicality of the solution both in terms of efficiency and explanation quality, compared to existing algorithms.

One important issue in modern database applications is supporting the user with efficient tools to debug and fix queries because such tasks are both time and skill demanding. One particular problem is known as Why-Not question and focusses on the reasons for missing tuples from query results. The EFQ platform demonstrated here has been designed in this context to efficiently leverage Why-Not Answers polynomials , a novel approach that provides the user with complete explanations to Why-Not questions and allows for automatic, relevant query refinements.

International audience