PT Unknown
AU Carme Julia
   Angel Sappa
   Felipe Lumbreras
   Joan Serrat
   Antonio Lopez
TI An Adapted Alternation Approach for Recommender Systems
BT IEEE International Conference on e–Business Engineering,
PY 2008
BP 128–135
AB This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach.
ER