A Tutorial on Feature Interpretation in Recommender Systems
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- A Tutorial on Feature Interpretation in Recommender Systems
Recommendations
Tutorial: Sequence-Aware Recommender Systems
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceRecommender systems are widely used in online applications to help users find items of interest and help them deal with information overload. In this tutorial, we discuss the class of sequence-aware recommender systems. Differently from the traditional ...
Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender Systems
The traditional matrix factorisation (MF)-based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This ...
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
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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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