A Meta-Algorithm for Improving Top- N Prediction Efficiency of Matrix Factorization Models in Collaborative Filtering

YAĞCI A., Aytekin T., GÜRGEN S. F.

International Journal of Pattern Recognition and Artificial Intelligence, cilt.34, sa.3, 2020 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1142/s0218001420590077
  • Dergi Adı: International Journal of Pattern Recognition and Artificial Intelligence
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: approximate nearest neighbor search, learning to rank, Matrix factorization, recommender systems
  • Boğaziçi Üniversitesi Adresli: Evet

Özet

Matrix factorization models often reveal the low-dimensional latent structure in high-dimensional spaces while bringing space efficiency to large-scale collaborative filtering problems. Improving training and prediction time efficiencies of these models are also important since an accurate model may raise practical concerns if it is slow to capture the changing dynamics of the system. For the training task, powerful improvements have been proposed especially using SGD, ALS, and their parallel versions. In this paper, we focus on the prediction task and combine matrix factorization with approximate nearest neighbor search methods to improve the efficiency of top-N prediction queries. Our efforts result in a meta-algorithm, MMFNN, which can employ various common matrix factorization models, drastically improve their prediction efficiency, and still perform comparably to standard prediction approaches or sometimes even better in terms of predictive power. Using various batch, online, and incremental matrix factorization models, we present detailed empirical analysis results on many large implicit feedback datasets from different application domains.