Personalized ranking for video games based on online reviews: An S-Kano-TOPSIS method integrating requirement categories and public opinion

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing Pub Date : 2025-09-01 DOI:10.1016/j.entcom.2025.101029

Yanze Liu , Tian-Hui You , Junrong Zou , Yuan Yuan , Bing-Bing Cao

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引用次数: 0

Abstract

The rapid expansion of the video game market intensifies customers’ difficulty in selecting preference-aligned games. Although online reviews offer valuable insights, effectively leveraging this information remains challenging. To address this, we propose S-Kano-TOPSIS, a personalized ranking method for video games that integrates requirement categories and public opinion. First, BERTopic is used to extract customer requirements (CRs), and their performance is evaluated via sentiment analysis using a BW-CNN model. Then, SHAP is applied to quantify the influence of each CR on customer satisfaction. The Kano model is employed to adjust CR importance based on their influence patterns. Furthermore, to reflect real-world decision-making, we incorporate preference similarity by analyzing reviews of games similar to those the customer has played. Finally, TOPSIS is used to generate rankings tailored to individual needs. Experiments on 72,000 reviews from eight video games demonstrate that the proposed method surpasses baseline approaches across multiple evaluation metrics. These results suggest that S-Kano-TOPSIS offers a structured and quantifiable approach to personalized video game ranking.

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基于在线评论的电子游戏个性化排名：一种整合需求类别和公众意见的S-Kano-TOPSIS方法

电子游戏市场的快速扩张加剧了消费者选择符合偏好的游戏的难度。尽管在线评论提供了有价值的见解，但有效地利用这些信息仍然具有挑战性。为了解决这个问题，我们提出了S-Kano-TOPSIS，这是一种集成了需求类别和公众意见的视频游戏个性化排名方法。首先，使用BERTopic提取客户需求（cr），并使用BW-CNN模型通过情感分析对其性能进行评估。然后，运用SHAP量化各CR对顾客满意度的影响。利用Kano模型根据其影响模式来调整CR的重要性。此外，为了反映现实世界的决策，我们通过分析与用户玩过的游戏相似的游戏评论来结合偏好相似性。最后，TOPSIS用于根据个人需求生成排名。对来自8款电子游戏的72000条评论进行的实验表明，所提出的方法在多个评估指标上优于基线方法。这些结果表明，S-Kano-TOPSIS为个性化电子游戏排名提供了一种结构化和可量化的方法。

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来源期刊

Entertainment Computing Computer Science-Human-Computer Interaction

CiteScore

5.90

自引率

7.10%

发文量

期刊介绍： Entertainment Computing publishes original, peer-reviewed research articles and serves as a forum for stimulating and disseminating innovative research ideas, emerging technologies, empirical investigations, state-of-the-art methods and tools in all aspects of digital entertainment, new media, entertainment computing, gaming, robotics, toys and applications among researchers, engineers, social scientists, artists and practitioners. Theoretical, technical, empirical, survey articles and case studies are all appropriate to the journal.