Bayesian Personalized Feature Interaction Selection for - PowerPoint PPT Presentation

Sep 19, 2022 •127 likes •260 views

Bayesian Personalized Feature Interaction Selection for Factorization Machines Yifan Chen, Pengjie Ren, Yang Wang, Maarten de Rijke The main author Main author: Yifan Chen Defended PhD thesis at the University of Amsterdam on October 8,

Bayesian Personalized Feature Interaction Selection for Factorization Machines Yifan Chen, Pengjie Ren, Yang Wang, Maarten de Rijke
The main author • Main author: Yifan Chen • Defended PhD thesis at the University of Amsterdam on October 8, 2018 • Now with NUDT, Changsha, China • yfchen@nudt.edu.cn
The paper • Yifan Chen, Pengjie Ren, Yang Wang, and Maarten de Rijke. Bayesian Personalized Feature Interaction Selection for Factorization Machines. In SIGIR 2019: 42nd international ACM SIGIR conference on Research and Development in Information Retrieval , page 665–674. ACM, July 2019 • https://sta ff .fnwi.uva.nl/m.derijke/wp-content/ papercite-data/pdf/chen-2019-bayesian.pdf
The main points • We study personalized feature interaction selection for factorization machines • We propose a Bayesian personalized feature interaction selection method based on Bayesian variable selection • [We design an e ffi cient optimization algorithm based on Stochastic Gradient Variational Bayes]
  Some details • Factorization machines: • Generic supervised learning method • Used for classification and regression • Account for feature interactions with factored parameters • Usually trained using SGD, ALS, MCMC   •
Not all interactions matter to all • E ff ective use of historical interactions between users and item • Incorporate additional information associated with users or items • High-dimensional feature space • #feature = #user + #item + #additional • Not all features or feature interactions are helpful
Not all interactions matter to all • x i are features • x 1 · x 2 are feature interactions • 4 × 4 matrices indicate masks for selection of feature interactions • one size fits all vs personalized selection
Rating prediction • Bias term • First-order interactions • Second-order interactions • Generic vs personalized
Bayesian generation model • To estimate personalized ratings   • Re-parameterization of interaction weights   • Use hereditary spike-and- slab prior to reduce number of candidate interactions
The results • Significant improvements over linear and non-linear factorization machines • Multiple datasets
A closer look • Examples based on MovieLens HetRec dataset • MovieLens 10M plus IMDB plus Rotten Tomatoes
What’s next? • Extend method to higher-order interactions or multi-view and multimodal factorizations • Consider group-level selections of interactions to speed up training • Paper available at: https://sta ff .fnwi.uva.nl/m.derijke/wp-content/ papercite-data/pdf/chen-2019-bayesian.pdf • Code available at: https://github.com/yifancli ff ord/BP-FIS

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