Collaborative Filtering with Label Consistent Restricted Boltzmann Machine

October 17, 2019 · Entered Twilight · 🏛 International Conference on Advances in Pattern Recognition

"Last commit was 9.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: Makefile, Makefile.100k, README.md, cfrbm, create_experiments.py, cross_validation.py, data_info.ipynb, decode_json.py, experiment_descriptions, for_other.py, movie_info_formator.py, tests, user_info_formator.py

Authors Sagar Verma, Prince Patel, Angshul Majumdar arXiv ID 1910.07724 Category cs.LG: Machine Learning Cross-listed cs.IR, cs.NE Citations 5 Venue International Conference on Advances in Pattern Recognition Repository https://github.com/sagarverma/LC-CFRBM Last Checked 2 months ago

Abstract

The possibility of employing restricted Boltzmann machine (RBM) for collaborative filtering has been known for about a decade. However, there has been hardly any work on this topic since 2007. This work revisits the application of RBM in recommender systems. RBM based collaborative filtering only used the rating information; this is an unsupervised architecture. This work adds supervision by exploiting user demographic information and item metadata. A network is learned from the representation layer to the labels (metadata). The proposed label consistent RBM formulation improves significantly on the existing RBM based approach and yield results at par with the state-of-the-art latent factor based models.