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首页> 外文会议>IEEE Congress on Evolutionary Computation >A Particle Swarm approach to mitigate the apparent diversity-accuracy dilemma in recommendation domains in recommendation domains
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A Particle Swarm approach to mitigate the apparent diversity-accuracy dilemma in recommendation domains in recommendation domains

机译：一种粒子群方法，可以在推荐领域推荐域中减轻表观分集准确态度的方法

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Advances in Recommender Systems (RSs) have been focused on improving the system's accuracy. However, accuracy alone is not enough to assess the practical effects. In real scenarios, diversity has been identified as a key dimension of recommendation utility. Thus, the main researches are focused in improve both, accuracy and diversity. This challenge remains an apparent dilemma that remains open and can boost sales by offering consumers both their mainstream and specific tastes. For this reason, we propose an approach to handle the accuracy-diversity dilemma. Our approach, based on a Particle Swarm Optimization (PSO), is a post-processing method to re-rank items from traditional RSs in order to improve diversity without accuracy losses. Experimental results in entertainment and e-commerce scenarios show that our strategy can improve users satisfaction. We improve the diversity up to 70% without significant accuracy losses.
机译：推荐系统的进步（RSS）一直专注于提高系统的准确性。然而，单独的准确性不足以评估实际效果。在实际情况下，多样性已被确定为推荐实用程序的关键维度。因此，主要研究专注于改善，准确性和多样性。这一挑战仍然是一个明显的困境，仍然是开放的，可以通过为他们的主流和特定品味提供消费者来提高销售。因此，我们提出了一种方法来处理准确性多样性的困境。我们的方法基于粒子群优化（PSO）是一种从传统RSS重新排名的后处理方法，以便在没有精确损失的情况下提高分集。娱乐和电子商务情景的实验结果表明，我们的策略可以提高用户满意度。我们将多样性提高到70％，而无明显的精度损失。

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