ABSTRACT
The Web is the largest public big data repository that humankind has created. In this overwhelming data ocean we need to be aware of the quality of data extracted from it. One important quality issue is data bias, which appears in different forms. These biases affect the (machine learning) algorithms that we design to improve the user experience. This problem is further exacerbated by biases that are added by these algorithms, especially in the context of recommendation and personalization systems. We give several examples, stressing the importance of the user context to avoid these biases.
Index Terms
- Data and algorithmic bias in the web
Recommendations
User Similarity Adjustment for Improved Recommendations
MIKE 2015: Proceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration - Volume 9468Recommender systems are becoming more and more attractive in both research and commercial communities due to Information overload problem and the popularity of the Internet applications. Collaborative Filtering, a popular branch of recommendation ...
Algorithmic bias in data-driven innovation in the age of AI
AbstractData-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, ...
Highlights- This study identifies the sources of algorithmic bias in data-driven innovations.
An effective social recommendation method based on user reputation model and rating profile enhancement
Trust-aware recommender systems are advanced approaches which have been developed based on social information to provide relevant suggestions to users. These systems can alleviate cold start and data sparsity problems in recommendation methods through ...
Comments