Robustness of collaborative recommendation based on association rule mining
By Jeff J. Sandvig, Bamshad Mobasher, Robin D. Burke. 2007
151 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Information Systems
- Xu Chen
- Yongfeng Zhang
- Diego Fernández
- Victor Carneiro
- Vreixo Formoso
- Fidel Cacheda
- Huseyin Polat
- Ihsan Günes
Citations in the corpus
- Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “Similarity, the Interpretable Convolutional Neural Network approach to explainable recommendation in Seo et al. [2017] developed a deep convolutional neural network-based model and display the item features to users as explanations, which falls into the category of deep learning-based method with us”
- A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, background): “In fact, many association rule models generate too many irrelevant or too few solid rules because they use a static support threshold [141, 143].”
- Comparison of collaborative filtering algorithms (2011, methodology): “Several techniques affecting both neighbor-based [Mobasher et al. 2007] and model-based [Sandvig et al. 2007] algorithms have been studied in the past.”
- Shilling attacks against recommender systems: a comprehensive survey (2012, methodology): “This way, similarity between users in the segment and injected profiles appears high, and so target item becomes more likely to be recommended (Burke et al. 2005c,d; Hurley et al. 2007; Mobasher et al. 2007a; Sandvig et al. 2007a).”
- Recommender Systems (2016, unknown): “For example, attackers might use knowledge of the criteria for detecting attack profiles [397], use obfuscated methods to mount attacks [631], or design attack methods targeting specific collaborative filtering models [522].”
- Recommendation Systems in Software Engineering (2014, background): “Other studies have shown that it is also possible to predict the severity or priority of issue reports [37, 44, 67], who should be assigned [3, 28, 40, 53], and duplicate bug reports [56, 58, 62, 64].”