کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6948421 | 1451040 | 2017 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
How can online marketplaces reduce rating manipulation? A new approach on dynamic aggregation of online ratings
ترجمه فارسی عنوان
چگونه بازار های آنلاین می توانند دستکاری امتیاز را کاهش دهند؟ یک رویکرد جدید در مورد تجمیع پویا از رتبه بندی آنلاین
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کلمات کلیدی
بازارهای الکترونیکی، رتبه های جعلی، روش جمعآوری رتبه، تقلب آنلاین، شبیه سازی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سیستم های اطلاعاتی
چکیده انگلیسی
Retailers' incentives to manipulate online ratings can undermine consumers' trust in online marketplaces. Finding ways to avoid fake ratings has become a fundamental problem. Most marketplaces update product ratings immediately, i.e., display new ratings as soon as they are submitted. Some platforms have proposed to reduce the frequency of rating updates, as hiding ratings for a certain amount of time allows identifying and eliminating bursts of suspicious ratings. Reducing the update frequency also allows aggregating ratings and displaying only a summary statistic (e.g., mean of ratings). Although such aggregation helps to reduce the amount of fake ratings, as multiple fake ratings get represented by only one value, it might also distort legitimate ratings from real customers and hence have negative impact on honest retailers. In the present study, we propose and evaluate a novel method that instead of displaying every new rating immediately, aggregates a sequence of most recent ratings to k-values, with k determined dynamically based on the distribution of the recent ratings. In a simulation, we demonstrate that our proposed method outperforms state-of-the-art aggregation methods - it effectively reduces the impact of fake ratings on sales, and at the same time only marginally affects sales of honest retailers. Our proposed method can be easily integrated in online rating systems and can be especially used for designing fraud-resistant ranking algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Decision Support Systems - Volume 104, December 2017, Pages 64-78
Journal: Decision Support Systems - Volume 104, December 2017, Pages 64-78
نویسندگان
Olga Ivanova, Michael Scholz,