Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity Bias
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- Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity Bias
Recommendations
Transparently Serving the Public: Enhancing Public Service Media Values through Exploration
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsIn the last few years, we have reportedly underlined the importance of the Public Service Media Remit for ZDF as a Public Service Media provider. Offering fair, diverse, and useful recommendations to users is just as important for us as being ...
New Recommendation Techniques for Multicriteria Rating Systems
Traditional single-rating recommender systems have been successful in a number of personalization applications, but the research area of multicriteria recommender systems has been largely untouched. Taking full advantage of multicriteria ratings in ...
Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings
Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias ...
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- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Association for Computing Machinery
New York, NY, United States
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