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首页> 外文期刊>Journal of Intelligent Information Systems >Reliable TF-based recommender system for capturing complex correlations among contexts
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Reliable TF-based recommender system for capturing complex correlations among contexts

机译：可靠的基于TF的推荐系统，用于捕获上下文之间的复杂关联

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摘要

Context-aware recommender systems (CARS) exploit multiple contexts to improve user experience in embracing new information and services. Tensor factorization (TF), a type of latent factor model, has achieved remarkable performance in CARS. TF learns latent representations of contexts by decomposing an observed rating tensor and combines the latent representations as a vector form to represent contextual influence on users and items. However, due to the limitation of the contextual expression power, they have difficulties in effectively capturing complex correlations among multiple contexts, and also the meaning of each context is diluted. To address the issue, we propose a reliable TF-based recommender system based on a proposed context tensor (CT-CARS), which incorporates a variety of correlations among contexts. CT-CARS contains a novel recommendation rating function and a learning algorithm. Specifically, the proposed context tensor elaborately captures the influences of both individual contexts and context combinations. Moreover, we introduce a novel parameter initialization based on past-learned results to improve the reliability of recommendations. The overall time complexity of our parameter learning algorithm grows linearly as dataset size increases. Experiments on six real-world datasets including two large-scaled datasets show that CT-CARS outperforms the existing state-of-the-art models in terms of both accuracy and reliability.
机译：上下文感知推荐系统（CARS）利用多种上下文来改善用户在拥抱新信息和服务方面的体验。张量因子分解（TF）是一种潜在因子模型，在CARS中取得了卓越的性能。 TF通过分解观察到的评级张量来学习上下文的潜在表示，并将这些潜在表示作为矢量形式进行组合，以表示上下文对用户和项目的影响。然而，由于上下文表达能力的限制，它们难以有效地捕获多个上下文之间的复杂关联，并且每个上下文的含义也被稀释了。为了解决这个问题，我们基于一个拟议的上下文张量（CT-CARS），提出了一个可靠的基于TF的推荐系统，该系统将上下文之间的多种相关性结合在一起。 CT-CARS包含新颖的推荐评级功能和学习算法。具体而言，拟议的上下文张量详细捕获了单个上下文和上下文组合的影响。此外，我们基于过去的学习成果介绍了一种新颖的参数初始化，以提高建议的可靠性。我们的参数学习算法的总体时间复杂度随着数据集大小的增加而线性增加。在包括两个大型数据集的六个真实数据集上进行的实验表明，CT-CARS在准确性和可靠性方面均优于现有的最新模型。

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