In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state-of-the-art performance. Yet, despite these advancements, award-winning research has ...
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ISBN:
(纸本)9798400706295
In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state-of-the-art performance. Yet, despite these advancements, award-winning research has demonstrated that traditional matrix factorization methods can remain competitive, offering simplicity and reduced computational overhead. Hybrid models, which combine matrix factorization with newer techniques, are increasingly employed to harness the strengths of multiple approaches. This paper proposes a novel ensemble method that unifies user-item and item-item recommendations through a weighted similarity framework to deliver top-N recommendations. Our approach is distinctive in its use of shared user and item embeddings for both recommendation strategies, simplifying the architecture and enhancing computational efficiency. Extensive experiments across multiple datasets show that our method achieves competitive performance and is robust in varying scenarios that favor either user-item or item-item recommendations. Additionally, by eliminating the need for embedding-specific fine-tuning, our model allows for the seamless reuse of hyperparameters from the base algorithm without sacrificing performance. This results in a method that is both efficient and easy to implement. Our open-source implementation is available at https://***/UFSCar-LaSID/weightedsims-recommender.
As large language models (LLMs) increasingly permeate educational applications, concerns about the perpetuation of bias persist. We present our preliminary work on developing prompt-engineering strategies to mitigate ...
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ISBN:
(纸本)9798400705328
As large language models (LLMs) increasingly permeate educational applications, concerns about the perpetuation of bias persist. We present our preliminary work on developing prompt-engineering strategies to mitigate bias in content generated by LLMs in computer science (CS) education. This work investigates both empirical insights into fairness-aware prompt formulation and actionable takeaways for educators. We focus on an initial list of prompting strategies for mitigating bias and explore their impact on educational content generation. Recent research has shown the efficacy of prompt-base debiasing [1] as well as the potential disadvantages of using prompts that have not been mitigated for bias, from user dissatisfaction [2] to unsafe outputs [5, 6]. Additionally, a growing body of empirical work points to the idea that certain properties of in-context examples such as flow [7], illustration [3], and order [4] could either improve or derail LLM performance. Our study leverages these findings in the context of generating educational content. The goal is to promote fairness-aware approaches which can be applied to the automated generation of learning materials and the development of LLM-based educational tools. This work also contributes practical insights on prompt-engineering to the evolving curriculum of Ethics in Artificial Intelligence (AI).
In this paper, we propose a new algorithm for computing transitive closures. It needs only O(e·b) time and O(n·b) space, where n represents the number of the nodes of a DAG (directed acyclic graph), e the nu...
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ISBN:
(纸本)9781581138122
In this paper, we propose a new algorithm for computing transitive closures. It needs only O(e·b) time and O(n·b) space, where n represents the number of the nodes of a DAG (directed acyclic graph), e the numbers of the edges, and b the DAG's breadth.
Travel has many situations where context-aware computing can bring important benefits. In this paper, we describe an approach for integrating context-aware computing to a mobile travel assistant. Travel plans, generat...
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ISBN:
(纸本)9781581138122
Travel has many situations where context-aware computing can bring important benefits. In this paper, we describe an approach for integrating context-aware computing to a mobile travel assistant. Travel plans, generated using reality [2], are enriched within compact and powerful structures, called User Task Models. These structures are transferred to a mobile device enabling the support for the traveler during his trip.
Today, OpenMP is the de facto standard for portable shared-memory programming supporting multiple levels of parallelism. Unfortunately, most of the current OpenMP implementations are not capable of fully exploiting mo...
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ISBN:
(纸本)9781581138122
Today, OpenMP is the de facto standard for portable shared-memory programming supporting multiple levels of parallelism. Unfortunately, most of the current OpenMP implementations are not capable of fully exploiting more than one level of parallelism. With the increasing number of processors available in high-performance computing resources, the number of applications that would benefit from multilevel parallelism is also increasing. Applying automatic differentiation to OpenMP programs is introduced as a new class of OpenMP applications with nested parallelism.
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