Interpretable Robust Recommender Systems with Side InformationDownload PDF


May 16, 2019 (edited Jun 12, 2019)ICML 2019 Workshop AMTL Blind SubmissionReaders: Everyone
  • Keywords: recommender systems, matrix completion, trace-norm regression, side information, interpretability
  • TL;DR: Methodologies for recommender systems with side information based on trace-norm regularization
  • Abstract: In this paper, we propose two methods, namely Trace-norm regression (TNR) and Stable Trace-norm Analysis (StaTNA), to improve performances of recommender systems with side information. Our trace-norm regression approach extracts low-rank latent factors underlying the side information that drives user preference under different context. Furthermore, our novel recommender framework StaTNA not only captures latent low-rank common drivers for user preferences, but also considers idiosyncratic taste for individual users. We compare performances of TNR and StaTNA on the MovieLens datasets against state-of-the-art models, and demonstrate that StaTNA and TNR in general outperforms these methods.
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