Collaborative Recommendation via Adaptive Association Rule Mining

By Weiyang Lin, Sergio A. Alvarez, Carolina Ruiz. 2000

113 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Chen
2. Yongfeng Zhang
3. Bamshad Mobasher
4. Honghua (Kathy) Dai
5. Tao Luo
6. Miki Nakagawa
7. Xiangnan He
8. Hanxiong Chen

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “[2005], Rennie and Srebro [2005]), Probabilistic Matrix Factorization (PMF) (Mnih and Salakhutdinov [2008], Salakhutdinov and Mnih [2008]), Localized Matrix Factorization (LMF) (Zhang et al. [2013b,a, 2014c]), etc. Matrix factorization methods are also commonly referred to as point-wise prediction m”
• Effective personalization based on association rule discovery from web usage data (2001, background): “Some recent studies have considered the use of association rule mining [2, 15] in recommender systems [6, 7, 17].”
• A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, unknown): “Lin, Alvarez, and Ruiz propose a method that produces more rules by adjusting the required support for individual users [92].”