Application of Machine Learning for Online Reputation Systems
This work addresses the need for better purchasing recommendations in online shopping by enhancing reputation systems, but it is incremental as it builds on existing weighted average methods.
The paper tackled the problem of improving product reputation scores in online systems by predicting consumer reliability from profiles and integrating it with weighted averaging, achieving promising results on MovieLens datasets.
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.