Predicting missing ratings in recommender systems: Adapted factorization approach

Carme Julià, Angel D. Sappa, Felipe Lumbreras, Joan Serrat, Antonio López

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach. Copyright © 2010 M.E. Sharpe, Inc.
Original languageEnglish
Pages (from-to)89-108
JournalInternational Journal of Electronic Commerce
Volume14
DOIs
Publication statusPublished - 1 Dec 2009

Keywords

  • Factorization technique
  • Recommender systems
  • Singular value decomposition

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