The present study demonstrates that CXCR4 gene transfer contributes to the enhanced in vivo reendothelialization capacity of EPCs. Up-regulation of CXCR4 in human EPCs may become a novel therapeutic target for endothelial repair.

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in particular, may suffer from both explanation bias and performance disparity. In this paper, we analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users, and that their recommendations may be biased by the training records of more active users, due to the nature of collaborative filtering, which leads to an unfair treatment by the system. We propose a fairness constrained approach via heuristic re-ranking to mitigate this unfairness problem in the context of explainable recommendation over knowledge graphs. We experiment on several real-world datasets with stateof-the-art knowledge graph-based explainable recommendation algorithms. The promising results show that our algorithm is not only able to provide high-quality explainable recommendations, but also reduces the recommendation unfairness in several respects.

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Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness problems in recommendations.Just like users have personalized preferences on items, users' demands for fairness are also personalized in many scenarios. Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands. Besides, previous works on fair recommendation mainly focus on associationbased fairness. However, it is important to advance from associative fairness notions to causal fairness notions for assessing fairness more properly in recommender systems. Based on the above considerations, this paper focuses on achieving personalized counterfactual fairness for users in recommender systems. To this end, we introduce a framework for achieving counterfactually fair recommendations through adversary learning by generating featureindependent user embeddings for recommendation. The framework allows recommender systems to achieve personalized fairness for users while also covering non-personalized situations. Experiments on two real-world datasets with shallow and deep recommendation algorithms show that our method can generate fairer recommendations for users with a desirable recommendation performance. CCS CONCEPTS• Computing methodologies → Machine learning; • Information systems → Recommender systems.

By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem. In this paper, we propose Counterfactual Explainable Recommendation (CountER), which takes the insights of counterfactual reasoning from causal inference for explainable recommendation. CountER is able to formulate the complexity and the strength of explanations, and it adopts a counterfactual learning framework to seek simple (low complexity) and effective (high strength) explanations for the model decision. Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed. These altered aspects constitute the explanation of why the original item is recommended. The counterfactual explanation helps both the users for better understanding and the system designers for better model debugging.Another contribution of the work is the evaluation of explainable recommendation, which has been a challenging task. Fortunately, counterfactual explanations are very suitable for standard quantitative evaluation. To measure the explanation quality, we design two types of evaluation metrics, one from user's perspective (i.e. why the user likes the item), and the other from model's perspective (i.e. why the item is recommended by the model). We apply our counterfactual learning algorithm on a black-box recommender system and evaluate the generated explanations on five real-world datasets. Results show that our model generates more accurate and effective explanations than state-of-the-art explainable recommendation models. Source code is available at https://github.com/chrisjtan/counter.

Abstract-Dysfunction of early endothelial progenitor cells (EPCs) is responsible for impaired endothelial repair capacity after arterial injury in patients with hypertension. Here, we hypothesized that diminished signaling of CXC chemokine receptor 7 (CXCR7) contributes to the reduced EPC functions, and enhanced CXCR7 expression restores the capacities of EPCs from hypertensive patients. CXCR7 expression of EPCs from hypertensive patients was significantly reduced when compared with that from healthy subjects. Meanwhile, the phosphorylation of p38 mitogen-activated protein kinase, a downstream signaling of CXCR7, was elevated, which increased cleaved caspase-3 level of EPCs. CXCR7 gene transfer augmented CXCR7 expression and decreased the phosphorylation of p38 mitogen-activated protein kinase, which was paralleled to EPC functional upregulation of in vitro adhesion, antiapoptosis activities, and in vivo re-endothelialization capacity in a nude mouse model of carotid artery injury. The enhanced in vitro and in vivo functions of EPCs were markedly inhibited by neutralizing monoclonal antibody against CXCR7, which was blocked by p38 mitogen-activated protein kinase inhibitor SB203580. Downregulation of cleaved caspase-3 level induced by CXCR7 gene transfer or SB203580 pretreatment improved EPC functions. Furthermore, we found that lercanidipine, a dihydropyridine calcium channel antagonist, enhanced CXCR7 expression and facilitated in vitro and in vivo functions of EPCs. Our study demonstrated for the first time that diminished CXCR7 signal at least partially contributes to the reduced in vitro functions and in vivo re-endothelialization capacity of EPCs from hypertensive patients. Upregulation of CXCR7 expression induced by gene transfer or lercanidipine treatment may be a novel therapeutic target for increased endothelial repair capacity in

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