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Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations
(Elsevier, 2022)
Collaborative filtering recommendation algorithms are vulnerable against the popularity bias, including the most popular items repeatedly into the produced ranked lists. However, the research on popularity bias focuses ...
Evaluating unfairness of popularity bias in recommender systems: A comprehensive user-centric analysis
(Elsevier, 2022)
The popularity bias problem is one of the most prominent challenges of recommender systems,
i.e., while a few heavily rated items receive much attention in presented recommendation
lists, less popular ones are underrepresented ...
Exploring potential biases towards blockbuster items in ranking-based recommendations
(Springer, 2022)
Popularity bias is defined as the intrinsic tendency of recommendation algorithms to feature popular items more than unpopular ones in the ranked lists lists they produced. When investigating the adverse effects of popularity ...
End to End Invoice Processing Application Based on Key Fields Extraction
(IEEE, 2022)
In this paper, an automatic invoice processing system, which is in great demand among private and public companies, was proposed. The proposed system supports all invoice file types that can be submitted by companies. ...
Aggregating user preferences in group recommender systems: A crowdsourcing approach
(Elsevier, 2022)
We present that group recommendations are similar to crowdsourcing, where the responses of different crowd workers are aggregated in the absence of ground truth. With this in mind, we mimic the use of the EM algorithm as ...