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ADC@𝜃r: Adaptive divisional categorization of ratings under rating threshold 𝜃r for similarity computation in recommendation systems
Applied Intelligence ( IF 5.3 ) Pub Date : 2021-06-04 , DOI: 10.1007/s10489-021-02428-w
Ankush Jain , Surendra Nagar , Pramod Kumar Singh , Joydip Dhar

In the recommendation systems (RSs), it is imperative to incorporate the hidden contextual meaning of users’ provided ratings in the similarity computation. To draw such contextual meanings, existing models use the fixed categorization of accessible ratings. However, due to the excessive variation in similarly co-rated item pairs, they produce ambiguous contextual meanings that yield inconsistent results for the user pairs similarities. Therefore, to deal with this problem, this paper proposes an adaptive divisional categorization (ADC)-based RS, namely ADC@𝜃r, that obtains the optimal contextual divisions of accessible ratings under the rating threshold 𝜃r. Here, accessible ratings are the numerical scores that RS users use to express their preferences on the underlying items. A set of adaptive divisions under rating threshold 𝜃r is termed optimal if most of its rating divisions cover a large portion of rating records of a given dataset. For so, the proposed ADC@𝜃r model keeps only those divisions of ratings whose significance values are high, i.e., cover a large portion of the rating records by the ratings of these divisions. Further, the contextual mean square deviation (CMSD) model is employed to compute user pairs’ similarity using the obtained adaptive divisions of accessible ratings. The experimental results obtained on the benchmark Movielens-100K and Movielens-1M datasets justify the proposed model’s superiority over the competitive models.



中文翻译:

ADC@𝜃r:用于推荐系统中相似度计算的评分阈值 𝜃r 下的自适应划分分类

在推荐系统 (RS) 中,必须在相似度计算中包含用户提供的评分的隐藏上下文含义。为了绘制这种上下文含义,现有模型使用可访问评级的固定分类。然而,由于类似共同评价的项目对的过度变化,它们会产生模糊的上下文含义,从而对用户对的相似性产生不一致的结果。因此,为了解决这个问题,本文提出了一种基于自适应划分分类(ADC)的 RS,即 ADC@ 𝜃 r,它在评分阈值𝜃 r下获得可访问评分的最佳上下文划分. 在这里,可访问评级是 RS 用户用来表达他们对基础项目偏好的数字分数。一组在评级阈值𝜃 r下的自适应划分被称为最优,如果它的大部分评级划分覆盖了给定数据集的大部分评级记录。为此,建议的 ADC@ 𝜃 r模型只保留那些显着性值高的评分分区,即这些分区的评分覆盖了大部分评分记录。此外,上下文均方偏差(CMSD)模型用于使用获得的可访问评级的自适应划分来计算用户对的相似性。在基准 Movielens-100K 和 Movielens-1M 数据集上获得的实验结果证明了所提出的模型优于竞争模型。

更新日期:2021-06-04
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