Online commodity recommendation model for interaction between user ratings and intensity-weighted hierarchical sentiment: A case study of LYCOM

IF 6.7 2区管理学 Q1 MANAGEMENT

Omega-international Journal of Management Science Pub Date : 2024-07-22 DOI:10.1016/j.omega.2024.103161

Chonghui Zhang , Na Zhang , Weihua Su , Tomas Balezentis

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引用次数: 0

Abstract

The online commodity recommendation (OCR) model mines users’ historical behavior characteristics and recommends products that may be of interest according to user preferences. Online reviews are among the most important information sources for OCR. However, the explicit and implicit emotional words in online review texts have different structures in the expression of multi-attribute emotions. To fully utilize review information and improve the recommendation accuracy, we propose an OCR model that considers the interaction of multiple attributes and hierarchical emotions and calculates a score weighted by emotion intensity. First, to balance the efficiency and accuracy of information extraction while considering the coexistence of explicit and implicit expressions in online review text, a multi-attribute hierarchical emotion lexicon construction method is proposed. Second, based on the advantage of intuitionistic fuzzy sets in terms of information expression superiority, multi-attribute review text information expression of the affective polarity and intensity of online review text is realized. Then, combined with the weighted singular value decomposition and factorization machine method, we propose an OCR model for interactions between multi-attribute emotions and scores through fusion and recombination of the eigenvectors of users and products. Finally, tourism products on the LYCOM website are used as an example to verify the effectiveness of the proposed method.

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用户评分与强度加权分层情感互动的在线商品推荐模型：LYCOM 案例研究

在线商品推荐（OCR）模型挖掘用户的历史行为特征，并根据用户偏好推荐其可能感兴趣的商品。在线评论是 OCR 最重要的信息来源之一。然而，在线评论文本中的显性情感词和隐性情感词在表达多属性情感时具有不同的结构。为了充分利用评论信息并提高推荐准确率，我们提出了一种考虑多属性和分层情感交互的 OCR 模型，并计算出按情感强度加权的分数。首先，为了兼顾信息提取的效率和准确性，同时考虑到在线评论文本中显性表达和隐性表达并存的特点，我们提出了一种多属性分层情感词典构建方法。其次，基于直觉模糊集在信息表达优越性方面的优势，实现了网络评论文本情感极性和强度的多属性评论文本信息表达。然后，结合加权奇异值分解和因式分解机方法，通过对用户和产品的特征向量进行融合重组，提出了多属性情感与评分交互的OCR模型。最后，以 LYCOM 的旅游产品为例，验证了所提方法的有效性。

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来源期刊

Omega-international Journal of Management Science 管理科学-运筹学与管理科学

CiteScore

13.80

自引率

11.60%

发文量

130

审稿时长

56 days

期刊介绍： Omega reports on developments in management, including the latest research results and applications. Original contributions and review articles describe the state of the art in specific fields or functions of management, while there are shorter critical assessments of particular management techniques. Other features of the journal are the "Memoranda" section for short communications and "Feedback", a correspondence column. Omega is both stimulating reading and an important source for practising managers, specialists in management services, operational research workers and management scientists, management consultants, academics, students and research personnel throughout the world. The material published is of high quality and relevance, written in a manner which makes it accessible to all of this wide-ranging readership. Preference will be given to papers with implications to the practice of management. Submissions of purely theoretical papers are discouraged. The review of material for publication in the journal reflects this aim.