A fuzzy approach to review-based recommendation: Design and optimization of a fuzzy classification scheme based on implicit features of textual reviews

IF 1.9 4区 数学 Q1 MATHEMATICS
S. Hasanzadeh, Seyyed Mostafa Fakhrahmad, M. Taheri
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引用次数: 1

Abstract

In the design of recommender systems, it is believed that the set of reviews written by a user can somehow reveal his/her interests, and the content of an item can also be implied from its corresponding reviews. The present study attempts to model both the users and the items via extracting key information from the existing textual reviews. Based on this information, a fuzzy rule-based classifier is designed and tuned, which aims to predict whether a typical user will be interested in a typical item or not. For this purpose, the set of all reviews belonging to a user are mapped to a vector representing the user's interests. Similarly, the set of reviews written by different users over an item are merged and mapped to a vector representing the item. By conjoining these two vectors, a longer vector is obtained which will be used as the input of the classifier. To optimize the classifier, an adaptive approach is suggested and rule-weight learning is carried out, accordingly. The performance of the proposed fuzzy recommender system was evaluated on the Amazon dataset. Experimental results narrate from the promising classification ability of the proposed recommender system compared to state of the art.
基于评论的模糊推荐方法:基于文本评论隐式特征的模糊分类方案设计与优化
在推荐系统的设计中,人们认为用户所写的评论集可以以某种方式揭示他/她的兴趣,并且从相应的评论中也可以暗示一个项目的内容。本研究试图通过从现有的文本评论中提取关键信息来建立用户和项目的模型。基于这些信息,设计并调整了一个基于模糊规则的分类器,其目的是预测一个典型用户是否会对一个典型项目感兴趣。为此,属于用户的所有评论的集合被映射到表示用户兴趣的向量。类似地,由不同用户对一个项目编写的评论集被合并并映射到表示该项目的向量。将这两个向量连接起来,得到一个更长的向量,作为分类器的输入。为了优化分类器,提出了一种自适应方法,并相应地进行了规则权重学习。在Amazon数据集上对所提出的模糊推荐系统的性能进行了评价。实验结果表明,与现有的推荐系统相比,所提出的推荐系统具有良好的分类能力。
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来源期刊
CiteScore
3.50
自引率
16.70%
发文量
0
期刊介绍: The two-monthly Iranian Journal of Fuzzy Systems (IJFS) aims to provide an international forum for refereed original research works in the theory and applications of fuzzy sets and systems in the areas of foundations, pure mathematics, artificial intelligence, control, robotics, data analysis, data mining, decision making, finance and management, information systems, operations research, pattern recognition and image processing, soft computing and uncertainty modeling. Manuscripts submitted to the IJFS must be original unpublished work and should not be in consideration for publication elsewhere.
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