Adherence and Constancy in LIME-RS Explanations for Recommendation

Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In particular, post-hoc approaches have proved to be the most easily applicable ones to increasingly complex recommendation models, which are then treated as black boxes. The most recent literature has shown that for post-hoc explanations based on local surrogate models, there are problems related to the robustness of the approach itself. This consideration becomes even more relevant in human-related tasks like recommendation. The explanation also has the arduous task of enhancing increasingly relevant aspects of user experience such as transparency or trustworthiness. This paper aims to show how the characteristics of a classical post-hoc model based on surrogates is strongly model-dependent and does not prove to be accountable for the explanations generatedThe authors acknowledge partial support of PID2019-108965GB-I00, PONARS01_00876BIO-D,CasadelleTecnologie mergenti della Cittàdi Matera, PONARS01_00821FLET4.0, PIAServiziLocali2.0,H2020Passapartout-Grantn. 101016956, PIAERP4.0,andIPZS-PRJ4_IA_NORMATIV

Explanation in Multi-Stakeholder Recommendation for Enterprise Decision Support Systems

Business agility requires support from recommendation systems, but explaining recommendations may yield information disclosure. We analyze how to provide explanations in the scenario of Multi-Stakeholder Recommendation where the sensible information of one stakeholder should not be disclosed in the explanation to another stakeholder. Among the several types of explanations analyzed, counterfactual explanations come off best as they allow the system to preserve each stakeholder’s privacy and sensitive information in terms of preferences

Semantic interpretability of latent factors for recommendation

Model-based approaches to recommendation have proven to be very accurate. Unfortunately, exploiting a latent space we miss references to the actual semantics of recommended items. In this extended abstract, we show how to initialize latent factors in Factorization Machines by using semantic features coming from a knowledge graph in order to train an interpretable model. Finally, we introduce and evaluate the semantic accuracy and robustness for the knowledge-aware interpretability of the model

On the discriminative power of hyper-parameters in cross-validation and how to choose them

Hyper-parameters tuning is a crucial task to make a model perform at its best. However, despite the well-established methodologies, some aspects of the tuning remain unexplored. As an example, it may afect not just accuracy but also novelty as well as it may depend on the adopted dataset. Moreover, sometimes it could be sufcient to concentrate on a single parameter only (or a few of them) instead of their overall set. In this paper we report on our investigation on hyper-parameters tuning by performing an extensive 10-Folds Cross-Validation on MovieLens and Amazon Movies for three well-known baselines: User-kNN, Item-kNN, BPR-MF. We adopted a grid search strategy considering approximately 15 values for each parameter, and we then evaluated each combination of parameters in terms of accuracy and novelty. We investigated the discriminative power of nDCG, Precision, Recall, MRR, EFD, EPC, and, fnally, we analyzed the role of parameters on model evaluation for Cross-Validation

Physiologic effects of positive end-expiratory pressure in patients with chronic obstructive pulmonary disease during acute ventilatory failure and controlled mechanical ventilation

Dynamic hyperinflation and intrinsic positive end-expiratory pressure (PEEPi) are observed in patients with chronic obstructive pulmonary disease (COPD) and flow limitation. Several reports suggest that PEEP levels approaching PEEPi reduce inspiratory load due to PEEPi, without further hyperinflation. Hence PEEP should not increase intrathoracic pressure or affect hemodynamics and gas exchange. To verify this hypothesis, the effects of PEEP (0 to 15 cm H2O) on respiratory mechanics, hemodynamics, and gas exchange were studied in nine COPD patients during controlled mechanical ventilation. PEEP levels approaching PEEPi (9.8 +/- 0.5 cm H2O) did not affect the expiratory flow/volume relationship, confirming the presence of flow limitation. PEEP levels of 5 and 10 cm H2O did not change lung volume and PEEPi in the respiratory system (PEEPtot,rs) and chest wall (PEEPtot,cw) or affect hemodynamics and gas exchange. When applied PEEP overcame PEEPi, changes in lung volume and the expiratory flow/volume relationship were observed. PEEPtot,rs and PEEPtot,cw also increased. Under these circumstances, PEEP increased static elastance in both the respiratory system and the chest wall, reducing cardiac index and affecting hemodynamics and gas exchange. Our data show that in mechanically ventilated COPD patients with PEEPi due to flow limitation, PEEP levels exceeding the 85% of PEEPi (Pcrit) caused further hyperinflation and compromised hemodynamics and gas exchange

Physiologic effects of positive end-expiratory pressure in patients with chronic obstructive pulmonary disease during acute ventilatory failure and controlled mechanical ventilation

Dynamic hyperinflation and intrinsic positive end-expiratory pressure (PEEPi) are observed in patients with chronic obstructive pulmonary disease (COPD) and flow limitation. Several reports suggest that PEEP levels approaching PEEPi reduce inspiratory load due to PEEPi, without further hyperinflation. Hence PEEP should not increase intrathoracic pressure or affect hemodynamics and gas exchange. To verify this hypothesis, the effects of PEEP (0 to 15 cm H2O) on respiratory mechanics, hemodynamics, and gas exchange were studied in nine COPD patients during controlled mechanical ventilation. PEEP levels approaching PEEPi (9.8 +/- 0.5 cm H2O) did not affect the expiratory flow/volume relationship, confirming the presence of flow limitation. PEEP levels of 5 and 10 cm H2O did not change lung volume and PEEPi in the respiratory system (PEEPtot,rs) and chest wall (PEEPtot,cw) or affect hemodynamics and gas exchange. When applied PEEP overcame PEEPi, changes in lung volume and the expiratory flow/volume relationship were observed. PEEPtot,rs and PEEPtot,cw also increased. Under these circumstances, PEEP increased static elastance in both the respiratory system and the chest wall, reducing cardiac index and affecting hemodynamics and gas exchange. Our data show that in mechanically ventilated COPD patients with PEEPi due to flow limitation, PEEP levels exceeding the 85% of PEEPi (Pcrit) caused further hyperinflation and compromised hemodynamics and gas exchange