RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender Systems
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- RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender Systems
Recommendations
Exploiting Social Tagging in a Web 2.0 Recommender System
Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data miningThe goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
A trust-enhanced recommender system application: Moleskiing
SAC '05: Proceedings of the 2005 ACM symposium on Applied computingRecommender Systems (RS) suggests to users items they will like based on their past opinions. Collaborative Filtering (CF) is the most used technique to assess user similarity between users but very often the sparseness of user profiles prevents the ...
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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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