![]() ![]() ![]() The term describes a style of leadership that blends a paradoxical mix of qualities. The concept of “level five leadership” is borrowed from the book Good to Great, written by Jim Collins. We aspire to develop a culture of “level five leadership” across the organization, fostering a blend of great humility with intense professional will. To work toward greater educational equity for all, we must deeply understand and enable the conditions to promote diversity, equity, inclusivity and belonging (DEIB) in our organization, and in the communities we serve. We recognize that unequal access to opportunity along lines of race, class, gender, sexual orientation, ability, age and other aspects of identity has deep roots in our country-including in our education system and all the systems that we operate in-creating persistent and deep inequities. We dedicate ourselves to building a more just, equal, fair and compassionate world. ![]() Because our experience has shown that young people surpass the highest of expectations to achieve extraordinary results. In the process, we’re striving to help foster a generation of empowered, experienced, effective and idealistic leaders. At City Year, we’re committed to tapping this power to help create more equitable access to education opportunities for students in systemically under-resourced schools. From advancing social justice in the United States, to youth-led movements around the world, the dedication of young people is driving major change. The energy and idealism of young people are two of the most powerful and transformative forces at work in the world today. We are committed to harnessing one of the most powerful forces for positive change at work in the world today. We recognize that this goal can only be achieved by working together with others-teachers, administrators, parents, policymakers and partners-who share our commitment to creating environments that will help students build on their strengths and fully engage in their learning. We are fiercely committed to dedicating our resources and energy in an effort to positively impact the lives of our students and their families, while supporting the success of our AmeriCorps members. The success of the young people we serve is our preeminent goal, best achieved by working in partnership with others who are dedicated to the same cause. At City Year we make a collective effort to demonstrate the power of service as a way to connect us to the fullness of what democracy and citizenship should be-teaching us the shared responsibility we have to each other and showing us what can be achieved through collaboration and determination. It’s also a way to bring together diverse individuals who share a common goal while strengthening our country and our communities. We dedicate ourselves to addressing shared civic challenges through unified action.Ĭity Year believes that service is a personal decision to dedicate one’s time, energy, and effort to a cause greater than oneself. Our journey to fully live our values and our commitment to DEIB is ongoing, but with each step forward, we come closer to actualizing our mission and fulfilling our promises to one another. The organizational values we collectively uphold are deeply rooted in this belief and in our commitment to diversity, equity, inclusion and belonging (DEIB). We use four large-scale datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation algorithms for social collaborative filtering based on trust.City Year was founded with a core belief: that uniting and empowering diverse teams of idealistic young people and charging them with addressing some of our country’s most difficult challenges can change the world for the better. This is a model-based method that adopts matrix factorization technique that maps users into low-dimensional latent feature spaces in terms of their trust relationship, and aims to more accurately reflect the users reciprocal influence on the formation of their own opinions and to learn better preferential patterns of users for high-quality recommendations. To address such issues, we propose a novel method that works to improve the performance of collaborative filtering recommendations by integrating sparse rating data given by users and sparse social trust network among these same users. ![]() Collaborative filtering is a widely adopted approach to recommendation, but sparse data and cold-start users are often barriers to providing high quality recommendations. Recommender systems are used to accurately and actively provide users with potentially interesting information or services. ![]()
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