Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach
Carme Julià, Angel D. Sappa, Felipe Lumbreras, Joan Serrat, and Antonio López
International Journal of Electronic Commerce,
Volume 14 Number 2, Winter 2009-10, pp. 89.
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.
Key Words and Phrases: Factorization technique, recommender systems, singular value decomposition.