Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Previous unexpected recommendation methods only focus on the straightforward relations between current recommendations and user expectations by modeling unexpectedness in the feature space, thus resulting in the loss of accuracy measures to improve unexpectedness performance. In contrast to these prior models, we propose to model unexpectedness in the latent space of user and item embeddings, which allows us to capture hidden and complex relations between new recommendations and historic purchases. In addition, we develop a novel Latent Closure (LC) method to construct a hybrid utility function and provide unexpected recommendations based on the proposed model. Extensive experiments on three real-world datasets illustrate superiority of our proposed approach over the state-of-the-art unexpected recommendation models, which leads to significant increase in unexpectedness measure without sacrificing any accuracy metric under all experimental settings in this article.

中文翻译：

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潜在的意外建议

意外推荐系统是解决过滤气泡和用户无聊问题的重要工具，旨在同时向目标用户提供意外和满意的推荐。以前的意外推荐方法只关注当前推荐与用户期望之间的直接关系，通过在特征空间中对意外进行建模，从而导致准确性措施的损失，以提高意外性能。与这些先前的模型相比，我们建议对用户和项目嵌入的潜在空间中的意外情况进行建模，这使我们能够捕捉新推荐和历史购买之间的隐藏和复杂关系。此外，我们开发了一种新的潜在闭包 (LC) 方法来构建混合效用函数，并基于所提出的模型提供意想不到的建议。在三个真实世界数据集上进行的大量实验说明了我们提出的方法优于最先进的意外推荐模型，这会导致意外测量显着增加，而不会牺牲本文所有实验设置下的任何准确度指标。