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Asymmetric response aggregation heuristics for rating prediction and recommendation
Applied Intelligence ( IF 5.3 ) Pub Date : 2020-01-20 , DOI: 10.1007/s10489-019-01594-2
Shujuan Ji , Wei Yang , Shenghui Guo , Dickson K.W. Chiu , Chunjin Zhang , Xinyue Yuan

User-based collaborative filtering is widely used in recommendation systems, which normally comprises three steps: (1) finding the nearest conceptual neighbors, (2) aggregating the neighbors’ ratings to predict the ratings of unrated items, and (3) generating recommendations based on the prediction. Existing algorithms mainly focus on steps 1 and 3 but neglect subtle treatments of aggregating neighbors’ suggestions in step 2. Based on the discovery of psychology that (i) users’ responses to positive and negative suggestions are different, and (ii) users may respond differently from one another, this paper proposes a Personal Asymmetry Response-based Suggestions Aggregation (PARSA) algorithm, which first uses a linear regression method to learn each user’s response to negative/positive suggestions from neighbors and then uses a gradient descent algorithm for optimizing them. In addition, this paper designs an Identical Asymmetry Response-based Suggestions Aggregation (IARSA) baseline algorithm, which assumes that all the users’ responses to suggestions are identical as references to verify the key contribution of the heuristics employed in our PARSA algorithm that user may responses differently to positive and negative suggestions. Three sets of experiments are designed and implemented over two real-life datasets (i.e., Eachmovie and Netflix) to evaluate the performance of our algorithms. Further, in order to eliminate the influence of different similarity measures, this paper selects three kinds of similarity measures to discover neighbors. Experimental results demonstrate that most people indeed pay more attention to negative suggestions and our algorithms achieve better prediction and recommendation performances than the compared algorithms under various similarity measures.



中文翻译:

非对称响应聚合启发式算法用于评级预测和推荐

基于用户的协作过滤在推荐系统中得到广泛使用,通常包括三个步骤:(1)查找最近的概念邻居,(2)汇总邻居的等级以预测未评级项目的等级,以及(3)基于根据预测。现有算法主要集中在步骤1和3,但在步骤2中忽略了汇总邻居建议的微妙处理。基于心理学发现,(i)用户对正面和负面建议的反应是不同的,并且(ii)用户可能会做出反应彼此不同,本文提出了一种基于个人不对称响应的建议聚合(PARSA)算法,该算法首先使用线性回归方法来了解每个用户对来自邻居的负面/正面建议的响应,然后使用梯度下降算法对其进行优化。此外,本文还设计了一种基于相同不对称响应的建议聚合(IARSA)基准算法,该算法假设所有用户对建议的响应均与参考相同,以验证用户在我们的PARSA算法中采用的启发式方法的关键作用对正面和负面建议的反应不同。在两个真实的数据集(即Everymovie和Netflix)上设计并实施了三组实验,以评估我们算法的性能。此外,为了消除不同相似性度量的影响,本文选择三种相似性度量来发现邻居。实验结果表明,大多数人确实更加关注否定建议,并且在各种相似性度量下,我们的算法比对比算法具有更好的预测和推荐性能。

更新日期:2020-04-20
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