Abstract
Online rating systems serve as decision support tool for choosing the right transactions on the internet. Consumers usually rely on others’ experiences when do transaction on the internet, therefore their feedbacks are helpful in succeeding such transactions. One important form of such feedbacks is the product ratings. Most online rating systems have been proposed either by researchers or industry. But there is much debate about their accuracies and stability. This paper looks at the accuracy and stability of set of common online rating systems over dense and sparse datasets. To accomplish that we used three evaluation measures namely, Mean Absolute Errors (MAE), Mean Balanced Relative Error (MBRE) and Mean Inverse Balanced Relative Error (MIBRE), in addition to Borda count to assess the stability of ranking among various rating systems. The results showed that both median and Dirichlet are the most accurate models for both sparse and dense datasets, whereas the BetaDR model is the most stable model across different evaluation measures. Therefore we recommend using Dirichlet or BetaDR for the products with few number of ratings and using the median model with products of large number of ratings.
Highlights
Online rating systems play a vital role in most ecommerce applications
The accurate rating system can let user choose the correct product which leads to better user satisfaction
From the obtained results we found that both median and Dirichlet are the most accurate models over dense and sparse datasets respectively
Summary
Online rating systems play a vital role in most ecommerce applications They help users to facilitate their decisions while they perform internet transactions [1], [4]. Many authors proposed different method to compute product score based on statistical and machine learning methods The accuracy of such methods depends mainly on the user satisfaction about the results achieved [14]. This satisfaction is difficult to be measured because most ecommerce application don’t provide a tool to evaluate the user satisfaction, and whether the given aggregate rating help them in performing the successful transaction. One of the common problem that faces rating systems is unfair ratings that biases aggregate scores for some products
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