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2 Annals of Computer Science and Information Systems

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Stereotype-Aaware Collaborative Filtering

100%

Frisch G. , Leger J.-B. , Grandvalet Y.

2021

tom Vol. 25

69--79

In collaborative filtering, recommendations are made using user feedback on a few products. In this paper, we show that even if sensitive attributes are not used to fit the models, a disparate impact may nevertheless affect recommendations. We propose a definition of fairness for the recommender system that expresses that the ranking of items should be independent of sensitive attribute. We design a co-clustering of users and items that processes exogenous sensitive attributes to remove their influence to return fair recommendations. We prove that our model ensures approximately fair recommendations provided that the classification of users approximately respects statistical parity.

Context Clustering-based Recommender Systems

67%

Kannout E.

tom Vol. 21

85--91

Recommender systems have gained lots of attention due to the rapid increase in the amount of data on the internet. Therefore, the demand for finding more advanced techniques to generate more useful recommendations becomes an urgent. The increasing need for generating more relevant recommendations led to the emergence of many novel recommendation systems, such as Context-aware Recommender System (CARS), which is based on incorporating the contextual information in recommendation systems. The goal of this paper is to propose new recommender systems that utilize the contextual information to find more relevant recommendations.