Fuzzy-Weighted Similarity Measures for Memory-Based Collaborative Recommender Systems

Mohammad Yahya H. Al-Shamri, N. Al-Ashwal
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引用次数: 23

Abstract

Memory-based collaborative recommender system (CRS) computes the similarity between users based on their declared ratings. However, not all ratings are of the same importance to the user. The set of ratings each user weights highly differs from user to user according to his mood and taste. This is usually reflected in the user’s rating scale. Accordingly, many efforts have been done to introduce weights to the similarity measures of CRSs. This paper proposes fuzzy weightings for the most common similarity measures for memory-based CRSs. Fuzzy weighting can be considered as a learning mechanism for capturing the preferences of users for ratings. Comparing with genetic algorithm learning, fuzzy weighting is fast, effective and does not require any more space. Moreover, fuzzy weightings based on the rating deviations from the user’s mean of ratings take into account the different rating scales of different users. The experimental results show that fuzzy weightings obviously improve the CRSs performance to a good extent.
基于记忆的协同推荐系统的模糊加权相似度度量
基于记忆的协同推荐系统(CRS)是基于用户声明的评分来计算用户之间的相似度。然而,并不是所有的评级对用户来说都同样重要。根据每个用户的心情和品味,每个用户所权重的评分集会因用户而异。这通常反映在用户的评分量表上。因此,已经做了许多努力来为crs的相似性度量引入权重。本文提出了基于记忆的CRSs最常见的相似度度量的模糊加权。模糊加权可以被视为一种学习机制,用于捕获用户的偏好进行评级。与遗传算法学习相比,模糊加权具有快速、有效、不占用空间等优点。此外,基于评分偏离用户评分均值的模糊权重考虑了不同用户的不同评分尺度。实验结果表明,模糊加权能较好地改善CRSs的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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