Robin D. Burke
ABSTRACT
This tutorial will discuss vulnerabilities of collaborative recommendation algorithms: attacks that can be mounted against them and possible defenses that can be used. The tutorial will be of interest to researchers and practitioners in the area of collaborative recommendation.
Index Terms
- Robust recommender systems
Recommendations
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 Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationMuch of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the ...
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