Application of Machine Learning for Online Reputation Systems
- URL: http://arxiv.org/abs/2209.04650v1
- Date: Sat, 10 Sep 2022 12:31:40 GMT
- Title: Application of Machine Learning for Online Reputation Systems
- Authors: Ahmad Alqwadri, Mohammad Azzeh, Fadi Almasalha
- Abstract summary: This paper proposes a new reputation system using machine learning to predict reliability of consumers from consumer profile.
The proposed model has been evaluated over three MovieLens benchmarking datasets, using 10-Folds cross validation.
- Score: 0.4125187280299248
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Users on the internet usually require venues to provide better purchasing
recommendations. This can be provided by a reputation system that processes
ratings to provide recommendations. The rating aggregation process is a main
part of reputation system to produce global opinion about the product quality.
Naive methods that are frequently used do not consider consumer profiles in its
calculation and cannot discover unfair ratings and trends emerging in new
ratings. Other sophisticated rating aggregation methods that use weighted
average technique focus on one or a few aspects of consumers profile data. This
paper proposes a new reputation system using machine learning to predict
reliability of consumers from consumer profile. In particular, we construct a
new consumer profile dataset by extracting a set of factors that have great
impact on consumer reliability, which serve as an input to machine learning
algorithms. The predicted weight is then integrated with a weighted average
method to compute product reputation score. The proposed model has been
evaluated over three MovieLens benchmarking datasets, using 10-Folds cross
validation. Furthermore, the performance of the proposed model has been
compared to previous published rating aggregation models. The obtained results
were promising which suggest that the proposed approach could be a potential
solution for reputation systems. The results of comparison demonstrated the
accuracy of our models. Finally, the proposed approach can be integrated with
online recommendation systems to provide better purchasing recommendations and
facilitate user experience on online shopping markets.
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